#WomenInTech – Sigrid Rouam

From genomic sequencing to finance or FMCG, Sigrid has been working in data science for many years. She shares invaluable insights on building the right team, dealing with a pace of change in technology, or the constant need to educate organizations on data science. Buckle-up!

Hi Sigrid, what do you do and what brought you to Singapore?

My job is Head of Data Science at the Singapore Exchange (SGX). I came to Singapore 11 years ago to complete my PhD in Statistics, jointly conducted at A*STAR (GIS) and Université Paris Sud. After that, I did a post doc at A*STAR in cancer genomics before moving to the private sector, gaining experience across several industries, namely FMCG, Telco and Finance. Initially the plan was to stay in Singapore for 6 months but 11 years later, I am still here!

You started your career in biological sciences, worked in a few different industries and are now in finance. What did you learn from working in these different industries?

Working in different industries has given me a wider knowledge on how to use data science to solve problems under different contexts.

Genomic sequencing was my first exposure to big data. Processing large amounts of data is not necessarily what statisticians care about (you usually leave this to engineers or computer scientists), but being able to deal with increasingly larger datasets is inevitable in today’s world where the amount of data created daily grows exponentially. Genomic data give rise to multiple testing comparison problem which, in statistics, occurs when a large number of hypothesis are made simultaneously. In the case of genomics, the expression levels of thousands of genes are measured at the same time. The more inferences are made, the more likely erroneous inferences are to occur. The multiple comparison issue needs to be tackled in order to obtain meaningful and reproducible results.

Working in the largest FMCG company in the world taught me how to develop a customer-oriented approach. Everything I did, I did it with the customer in mind. The CEO used to always remind us that “the customer is the boss”.

In Telco, I dealt with geolocation data and how to productise and monetise it. I also learnt how to deal with personal data and data privacy.

When I joined the financial industry, I discovered a whole new world. Indeed, financial data – trading data to be more specific – is very time sensitive. Everything happens so fast that speed and latency are critical. Financial data analysis requires  specific infrastructure and tools. Dealing with time series also brings additional complexity because what happened yesterday can’t be treated independently from what happens today. This is totally different than clinical research where each subject can be treated independently from the rest.

Even though some data science skills are transferable across different domains, context plays a crucial role too. Clearly understanding the problem requires data scientists to understand the industry.  in order to gather relevant data and draw relevant conclusions. Data scientist can have very strong technical skills, but if they can’t interpret models correctly and tell a story about the data in their respective contexts, all their efforts would be in vain.

When you joined SGX, you had to build everything from scratch, what did you enjoy the most?

First, building my own data science team from scratch, and second, having a strong support from the upper management.

Building a team means addressing four key areas from my perspective:

People

We have adopted a hybrid approach with a core data science team that reports to me and what we call super users from  different business units. The core team is built with diversity in mind. There is a mix of people with expertise in stats, computer science, engineering and finance. Because the team is quite small and the amount of work is large, we have trained the super users in coding. These super users help us do simpler tasks such as data retrieval, dashboards, reports tailored to their BU needs, and also gather requirements from their teams back to us. This enables us to have more bandwidth to do more advanced analytics.

Processes

Sound scientific principles require a set of best practices to guide data scientists in their daily activities. I created the SOPs and pushed to  adopt agile practices. Finally, the team has defined a set of analytics deployment processes, which are steps to deploy code from a development environment, to a testing environment (on real data), and finally to a production environment ( deployment to end users).

Technology

I spent the first year building a Machine Learning Platform which enables data scientists to run analytics at scale.The platform allows to process large amounts of data using parallel processing. It supports the most common coding languages used by data scientists which are R and Python. It is also satisfied all the security and compliance requirements.

Use-Cases

A big part of my role is to define use cases. In the beginning, the team built easy use cases, to prove  the value of data science and fund the analytics journey. Now that the team is bigger and processes mature, we can spend more time on both exploratory work (which aims at bringing innovative technologies to the exchange) and delivery mode (which works very closely with the different business units in order to cater to their respective needs, e.g. better consumer understanding, tasks automation).

Our company organization gives my team the unique opportunity to interact with the CEO and President. Their vision for the future is truly inspiring and acts as a real enabler for our team’s development.

The success of data initiatives in a company largely relies on the CEO’s support. It is critical to increase the adoption of data oriented mindset across the company. Creating a data team from scratch often implies  changing processes,, organization chart and the culture. If the CEO doesn’t buy in into the data transformation journey, data science team tends to spend more time convincing the organization than doing its job. . Data science is not all about technology, it is also about communication and change management.

After few experiences now, what main challenges do data leaders face in their role?

There are two main challenges

  • Convince people about the importance of data science for the company and get rid of misconceptions around data science
  • cope with a constantly changing environment where technology evolves very quickly.

On the first challenge, I adopted two key strategies.

First, we have to educate people. I organized sharing sessions on specific topics (e.g. what is AI/ML), demos on specific use cases as well as trainings on statistics, python and q (the coding language we use in the company to extract data from our database).

In addition, I put a lot of effort in building a data-driven culture, i.e. leverage data whenever and wherever possible in order to help business units to take  better decisions. One initiative that I started recently was to build a data science community in order to promote data and ideas exchange, encourage cross-disciplinary work, and brainstorm new ways to look into data.

To cope with a constantly changing technology landscape, I practice continuous learning. I love being challenged and acquiring new skills, be it technical skills, soft skills or business understanding. Learning is like playing a sport, the more you learn, the better you are at it and the more addicted you become to it too! When I moved to SGX, I had to pick up financial knowledge as well as a q programming language. Three months later, I was training people on q. The important thing is to stay humble without being afraid to start from scratch again and again.

As more and more data is used to train the Machine Learning algorithms, AI has made tremendous progress over the last few years. How can we ensure that AI is used for good?

In order to ensure that AI is used for good and in order to prove Elon Musk wrong when he says that AI is evil, we need to start thinking on how we can build AI in a sustainable manner.

In order to build a sustainable AI, i.e. an AI that is here to last, there are four aspects to keep in mind: AI should be inclusive and fair, ethical, responsible and explainable.

To be inclusive and fair, AI should benefit to everyone and no one should be left behind. AI should not discriminate and we should prevent biases. Private companies have made some progress and, for example, Google images no longer tags black people as chimpanzees.

An ethical AI means finding a balance between privacy and common good. Where should we stand between a world where nobody shares any data and where we know everything about everyone? If we don’t share any data, it becomes hard for models to be fair as they are not trained with a representative set. At the same time, if we reveal too much, our freedom may be impacted and the data we share may be used against us.

In order to build a responsible AI, our biases need to be properly managed. There are two types of biases to manage: biases we are conscious about and, more importantly, biases aren’t. Having a third party looking at the data or model built by data scientists would be of great help, what I call a “data psychologist” that, similarly to patient seeking the advice of a psychologist, would ask various questions around them to ensure that no biases are introduced.

Finally, AI should be explainable and transparent. For example, when a bank builds a credit scoring model, it should be able to explain why it is approving or rejecting applicants. More importantly, the decisions to accept or reject application should be consistent over time. As more and more data is used to train the Machine Learning algorithms, there is a risk that the final decision may change. If models and decision making processes can be explained, these problems are more likely to be avoided.

In order to ensure that AI is used for good and in a sustainable way, we, Data Scientists, should keep these four principles in mind when building Machine Learning algorithms. We can play a key role in ensuring that data is used appropriately and responsively.

What are the challenges of being a woman in data? How “Dare you” to be a woman in data?

People often ask me this question. Working in a male dominated environment can be very intimidating for some women and they want to know how I deal with it. Being in Technology, especially in the finance sector, I have always been one of the few women (this has been so since my Masters in Statistics). It is not just about knowing your domain very well, I think the main issue is related to self-confidence. Being self-confident is key if you want to convince people, influence and inspire them. People naturally tend to listen to people they respect or that inspire them.

Here are a few tips I often share with other females I mentor.  First work on aligning your message with your body language. Always get a sit at the table; don’t try to hide at the back of the room in the shadows where no one can see you. In meetings, don’t hesitate to voice up and join the conversation. People can’t read minds but understand words. Market yourself. What is the point of building the best analytical tool if nobody knows about it? Think about yourself a bit more and don’t be afraid to say no. Finally, get mentors (both male and female) to understand where your blind spots are.

How do you bring more diversity?

I have built a data science team based on talent diversity. All the team members have very different backgrounds, different cultures and complement each other very well.

I also encourage individual training, exchange of ideas via various means both offline (workshops, team bonding events, sharing sessions, stand ups) and online (chats, intranet, shared spaces).  Thanks to their complementarity, they constantly learn from each other and seek each other advice.

Our Technology division is also very diverse in terms of skills as well as gender. We have several women leading different teams. Our head of technology is also a female.

In data science, however, there is a clear lack of female talent. Most female I interview want to join the healthcare sector or smart city initiatives.

As a constant effort to bring in more diversity in the team, I am working hard in getting ladies on board.

Diversity in genetics allows species to evolve and adapt to changing environments.  In the same way,  diversity in the workplace is vital for organisations to stay relevant in a constantly changing world.

#WomenInTech – Isaline Duminil

Hi Isaline, so what do you do and what brought you to Asia ?

I am the Marketing and Communications Director for JCDecaux Singapore.

Exploring different countries has been a large part of my life having spent most of it in different places. My travels have brought me to so many beautiful cities – Paris, Dakar, Antananarivo, St Louis, New York City and Montreal. At some point I got “stuck” almost 10 consecutive years in Paris which was way too long for the globe-trotter I am, and I decided to move to Singapore 6 years ago.

How did you get started on data?

Data is a big word. Big in the sense that it can take so many forms and meanings Essentially, it is an information that can guide the next course of action.

I started in marketing development for a big cosmetic brand, which meant translating  market research to appealing products with a hint of creativity. I then quickly moved to what was at the time called “Communications and Internet”. Internet was fairly new and I was in charge, amongst other things, of social media. I had to build a voice for the brand, convince our business units that it was worth the effort and  introduce other metrics than sales figures to measure the ROI of our online activities.

In many organisations, the Marketing department leads digital transformation because it has direct access to customer data. Building upon my past experience in digital marketing, I know that there are always opportunities to create value and generate revenue by collecting and using information differently.

I am now in charge of overseeing data usage, streamlining approaches from collection, standardization to analytics , as well as championing data initiatives for the group. The main goals are to improve our accountability to advertisers, enhance user experience for the consumers, streamline our maintenance processes and contribute to citizen life.

As an example, for planning purposes we look at location characteristics, profile of the audience in that location, and then layer government statistics or insights from surveys that we run, as well as mobility patterns. For creative purposes we integrate live feeds of the weather, news, flight information, social media.

It is a very creative job!

We are also starting to look at how we can do predictive maintenance based on operations data that we collect.

It takes both business acumen and technical skills to derive insights and build a story from data, defining needs and making sure it is well understood by all. This can be challenging when language, culture and professional training are different among team members. Those projects require a lot of collaboration with other departments internally, such as IT and our corporate data scientists team.

Where does data come in for a traditional media like out-of-home?

Out-of-home (OOH) is about billboards, digital screens, in the streets, in bus shelters, in malls, in airports… It is one of the oldest forms of advertising and the and the simple reason why it prevails to this day is that it works. It is actually the traditional media that generates most online activation per dollar spent. That being said, unlike online, the key strengths of out-of-home are that it is a mass media, and is able to increase exposure among consumers due to the amount of time people spend outside their homes.

Online advertising has fragmented the audiences. Traditional media has to meet changing expectations by delivering more relevant and engaging ad content to consumers. Data links OOH’s capability for mass exposure with personalised advertising , attaining relevance at scale.

This new media landscape is not without its own set of challenges, among which transparency and accountability are the most glaring. The ubiquity of online media also translates in an increasing number of KPIs to measure marketing objectives achievements.

Measurement tools developed by JCDecaux, such as the Airport Audience Measurement, or  the Streetside Audience Measurement, address these concerns by delivering a set of KPIs such as reach, frequency and impressions. With our islandwide footprint in Singapore encompassing bus shelters, cinemas, premium malls, billboards and the airport, it is ever more important for advertisers to be able to adopt a data driven approach to optimise their OOH campaigns.

Our data comes from diverse sources – it comes from the asset itself and its location, which is an incredible source we can gather observations from, such as the airport for travellers and malls for shoppers. Data can also come from third parties, in such cases, our sources differ depending on the platform and we take efforts to ensure that we only work with the most relevant ones. One example of how we layer different data sets is the Streetside Audience Measurement, where we combine mobility patterns with out-of-home industry standards such as viewing distance, audience profiling and government statistics. From this, we are able to derive audience data that would form the basis of recommendations for campaign placement.

How is out-of-home keeping up with tech trends?

A shift towards a data-driven approach in OOH advertising paves the way for campaign optimisation and allows for personalisation at scale. With data, OOH advertising is able to track impressions and audience engagement to fulfil campaign objectives by allocating number of impressions to each campaign buy as well as offering ways to measure drive to store impact. In fact, data now can also fuel the creative process, driving increased awareness and brand recall.  

Today it is very easy to integrate live data feeds into the creative to trigger content specific to certain events. Possibilities include flight departure or arrival in an airport environment, news updates, social media feeds or just time and weather. We are looking forward to working to online retailers on the possibility of integrating a feed displaying the top trending products on an ecommerce platform as their out-of-home campaign, a seamless way to merge online and offline.

Going further, cameras can be used to anonymously track viewers impression and dwell time on specific category of audience. A brand could then use OOH to optimize its advert based on set KPIs of how a visual is likely to be received and if is what audiences want to see.

There is a huge emphasis on the consumer experience to create a holistic brand experience across multiple touchpoints, be it offline or online. Digital out-of-home is in a unique position to be able to converge online and offline worlds by being a part of the marketing mix and prompting online activity.  For example integrating mobile and OOH marketing strategies through location-based targeting delivers more than just commercial advantages. A Posterscope research led in 2016 showed that mobile click-through rates increased by up to 15% when supported by OOH, and a major piece of industry research conducted last year demonstrated that best performing OOH campaigns created a 38% uplift in short-term brand action taken via mobiles, with 66% of all actions being direct to the brand itself.

Where I feel really lucky is that I joined this industry at the right time and in the most exciting position. I have the chance to contribute to the digital transformation of this market leader.

How does JCDecaux integrate innovation on its digital transformation journey?

Start-ups are an amazing source of inspiration and revenue. Our team is constantly liaising with start-ups to develop our suite of innovative technological solutions. From sensors for traffic or maintenance purposes, solution for camera triggered content to mobile native adtech companies.  The numerous partnerships we have forged grants us access to technologies and a community that shares a thirst for innovation.

Among the challenges that start-ups face when upscaling are the localisation of their technology as well as market readiness. Our initiatives like Vivatech 2019, invits start-ups to pitch ideas on enhancing the lives of commuters through solutions that can be implemented at bus shelters. JCDecaux also started an initiative in the UK which was then rolled out in France called Nurture. This program offers mentoring on marketing and design, including campaign strategies and execution, artwork design and production.

We have started working with Live with AI this year, which aims to motivate corporates to take responsibility in using AI and find ways to enhance rather than overshadow human capabilities.

How dare you to be a woman in data?

I am a very curious person with an insatiable thirst to discover new things. I think I would have stayed in school all my life if I could. What I love about data today is that it’s the beginning of a new era and  applications to other fields are yet to be defined. There is still room for imagination. Just in my job I am applying it to 4 different fields, ranging from audience measurement, creative, business strategy to maintenance.

We are interacting with more data as it becomes an increasingly important aspect in our daily lives and our business. Given the vastness and complexity of what we are dealing with, there are plenty of opportunities for those with an interest to contribute to this expanding field. Those seeking a career in data no longer need to be confined to researcher or developer roles often dominated by men. “She loves data” is a good example of this. It has become a communication challenge I think, to let the new generations know that anybody can make it in any field. Apart from these, I think that the notion that “data is for geeks” is also getting debunked – I hope I am not offending anybody here, I find it super cool to work in  in data 😉.  

How do you bring more diversity?

There is a majority of males at most tech events we attend. However, most of my colleagues are women for some reason. I definitely have no qualms hiring men on my team. Although, actually my team may be the one that is the most diversified with 30% male and four different nationalities.

The fact is, I do not hire different people simply for the sake of diversity, but rather what they can offer in terms of skills, expertise, mindset and disruptive points of views.

Diversity may require more efforts from everyone to communicate, to make sure we are aligned and speaking the same language.

Nevertheless, we need different point of views to build strong solutions.  I truly believe in the saying “the whole is greater than the sum of its parts”.

#WomenInTech – Celine Le Cotonnec

Celine arrived 16 years ago as a student in Sinology and is now Chief Data and Innovation Officer at AXA Insurance in Singapore. Read this amazing story!

Hi Celine, so what do you do and what brought you to Singapore?

I came to Asia 16 years ago as a sinology student in a Taiwanese university. Learning Chinese since I was 14 years old was the best choice I have ever made. After an International Business degree at Guangzhou University, I found a job at the French Consulate Trade Commission in Shanghai, supporting SMEs in heavy industries and new technologies to develop in China. This was my first experience in Tech, I will always remember that day in 2007, when I met a small Chinese company developing a third-party mobile and online payment platform. There were less than 100 employees at that time. It was nothing else than…Alipay!

With a business related background, starting by sinology, how come you ended up working in data and innovation?

In my previous role in PSA Peugeot Citroen China, I was leading the digital and media buying for our three brands: Peugeot, Citroen and DS:overseeing the performance of e-commerce strategy, analytics and social media campaign. Data Science was first used in Digital Media to improve retargeting, offer customized advertisement,increase traffic and improve conversion.

Later on, I took over the Innovation Department for PSA in China. We launched our first connected car and were looking at creating innovative services for improved driving experience based on car sensors’ data. The digital ecosystem in China is drastically different from Europe. Customers are younger and more connected. We had to develop a suite of services suitable for Chinese customers and find new business models with digital partners using car data to generate additional revenues. This was the only way to maintain expensive car connectivity and cyber-security infrastructures. It was also the beginning of Mobility-as-a-service. Everyone witnessed the emergence of platforms such as Uber in Europe, Grab in South East Asia or the Giant Didi in China. Similarly to what happened in the hospitality industry with the appearance of Airbnb, we saw business models switching from ownership to pay-per-use. Every car manufacturer was wondering how to ride on the wave of mobility services on top of the traditional car purchase offer. I was then asked to create a Business Unit on connected services and mobility to address those questions for the Chinese market.

So what does it mean to be a Chief Data Officer?

The role of a Chief Data officer is mainly to transform the organization to be more data-driven.Moving away from gut feelings to analytics enabled organizations to measure, track and monitor the performance of our processes, products, distributions and customer experience. Thanks to real-time visibility on our business, each employee of the organization can take fast and sound decisions, monitor the results, and apply corrective actions. Analyzing customer data helps understand their habits, market segment, life style, so it becomes possible to design customized insurance with the right coverage, sized risk, at the appropriate time.

I oversee a real diversity of data-related activities including data management, data quality and strategy for Singapore. The team is responsible for data analytics and business intelligence, which means creating valuable insights from data. We are also leading IT topics on data platforms related to business or customers. On the innovation side, our projects ranges from developing machine learning algorithms, implementing NLP or deep-learning technics to extract value from voice, images, pdf and web data.

What is your strategy in Singapore to transform an organization into becoming data-driven? What are the main things an organization needs to focus on when embarking on a data transformation journey?

Singapore in the leader in South East Asia when it comes to innovation and AI, thanks to the government –led Smart Nation initiative. To strengthen and accelerate data-driven transformation in any traditional organization, I would recommend to first focused on building four enabling pillars:

1. Platform and Tools:

Any data professional needs an environment to work: a Data Lake. Last year, we moved our various data infrastructure to public cloud in order to benefit from on-demand storage, computing and services. We have also moved to agile project management. While several teams were coding in different languages, the decision was taken to streamline every legacy analytical codes we had into Python programming language because of its simplicity, community support and numerous libraries. Finally, Tableau was widely deployed as visualization tool, speeding up decision making and KPI tracking. Anybody in the organization ranging from data scientist, analysts to actuaries, can now perform independent statistical analysis, advanced analytics, create and deploy machine learning models, at a minimum cost, with a competitive speed, and positively impact our customer experience.

2. People and Culture.

Changing an organization is not just about switching to new tools. It is first and foremost about changing the mindset of employees, their ways of working and raising awareness on what data-driven actually means. This year we set the ambitious target to train 20% of the organization in Python and Tableau. Data champions within a business unit, also called “Super Users” would  undergo an intensive four months training provided by the data team with a strong mentoring during the first months. Directors, and even Executive Committee Members would also undergo a 15 hours crash course in Python and data analytics. For the rest of the employees, there would be challenges on raising data awareness.

At a global level, we developed partnerships with e-learning platform such as LinkedIn learning and Coursera to encourage everyone to improve their  data and analytics capabilities.

Finally, we hold numerous events such as: lunch & learn with speakers from outside the insurance industry, panel discussions, evening meet-ups, sharing session with other data science teams from other companies. The main goal is to communicate on new business opportunities enabled by new tech and AI. We want to involve and empower the whole company to be part of this . Change Management efforts are key to achieve a true mindset switch.  

3. Data Governance

Using and storing data also imply compliance with data privacy and security regulations. Recent scandals such as Cambridge Analytica and the Singapore Data Breaches, remind us that large-scale data collection and usage could potentially raise significant privacy concerns. AXA is today the most forward thinking insurer globally when it comes to responsible AI and use of data. We are contributing to the public debate through collective actions such as IMPACT AI library In Europe or LiveWithAI think-tank in Singapore. A strong Governance framework is critical to balance between value and data privacy in the digital age.

4. Data management

It is nowadays a growing activities even in non-online businesses. Lot of people do not understand that 80% of a data science project is getting access, collecting, cleaning, and understanding the data. Predictive modelling that is supposed to be the most exciting part, is actually less than 20% of a data scientist daily job.

Value comes with quality and uniform data, as well as comprehensive guidelines for upstream users.

Once the basics are in place, it is a matter of weeks or even days to launch, test and industrialize a new AI. I’d like to quote here, Dr Deb Goswami, lead Data scientist at Traveloka, main online travel platform in Asia :” For a data science team, developing AI models is not the end game, but the value of the problem you are trying to solve”

According to you, how will AI disrupt the Insurance industry? And are the insurers afraid of Insurtech start-ups?

I wouldn’t consider AI as a disruption as it will only improve insurers’ efficiency as they become more customer-centric. The real disruption, in my opinion, will come from technologies such as Blockchain because it reduces middlemen such as agents/brokers, insurers, or even banks. In a trusted environment, people could pool risk among peers and get them directly reinsured without any.  

In the current value chain of insurance, it starts with the customer, of course, who buys an insurance from a broker or agent, the product is priced and underwritten by an insurer, who gets part of the risk reinsured by a reinsurer.

In today’s insurtech market, I would say that 80% of the start-ups are digitalizing the distribution experience, disrupting the intermediary but most likely supporting the digital sale of traditional insurance product.

The remaining 20% are working on solutions that would improve the efficiency of an insurer: AI in fraud detection, video-consultation to reduce healthcare cost and improve the customer experience, damage recognition from a car accident in order to speed up the process of surveyor and settle the claims faster with the counter-part insurer.

In the current context, insurtech start-ups are partners that can enable insurers to speed up their transformation and offer a better digital experience for our customers.

What do you find the most inspiring in the future you foresee?

The deployment of IoT and AI across all industries, made possible at affordable cost through the upcoming launch of 5G, cloud computing, and the emergence of blockchain will accelerate collaboration between platforms. While industries used to compete and work in silos, the new trend is to refocus on customer and collaborate to improved user experience through API integration. Technologies will also improve natural resources management and optimize existing assets.

Take the example of personal cars. In average they are  only in use 6% of their lifetime. Parking are expensive for both urban planning and users, and 30% of the traffic in big cities, such as Paris, are caused by people looking for a parking space. With the emergence of autonomous vehicle, the world will have to produce less cars and emit less greenhouse gas. In this period of fast change, every industry needs to transform and reinvent itself if it wishes to remain relevant in the connected AI-driven world of tomorrow.

How “dare you” be a women in data and how do you bring more diversity?

Diversity is not only about gender parity. Diversity is also about recruiting people from other industries, with different skill sets or culture. Diversity in ages and experience is also a great value inside and organization. Managing people with more skills or more experience should not be a threat.

I do support several initiatives promoting gender diversity in the data world. I am working with SheLovesData, girlswhocode and mentoring young female talents. In the book written by Sheryl Sandberg, Lean In, the issues of women in leadership are well described: putting others before themselves, lack of networking skills and being afraid of reaching out to their network or refusing a job because you’re not sure to have 100% of the skills required. I’m quite proud of the female ratio we have in our team or within the global data family. Our group lead data architect is actually a woman and there are several women CDO in the region I’m reporting to. Be bold, take risks, don’t be afraid and fight for your values are the advices I would always give to young mentees.  #daretobeafemaleintech