Spela Hafner - Sharing Tribe

A guide to choosing the right data science position

Spela Hafner — Sun, 27 Sep 2020 06:34:43 +0000

Photo by ThisisEngineering RAEng on Uns plash

Article courtesy of Mısra Turp.

I have been interviewing people from all around the world on my podcast So you want to be a data scientist?. My guests have been from very different positions who all have either the title data scientist or similar. Inspired by everything I’ve heard from my guests on the podcasts, I prepared a list of possible positions you can consider when planning your future.

It’s important to know which one you want to work at, in order to get the most satisfaction out of your job. I understand this is not very easy to do when you have little to no experience in a field. In data science, you can end up in many different types of positions. They all have varying responsibilities, different careers and busyness. But if you are not in the type of position you enjoy, you might end up unhappy.

I didn’t think about this much when I first started. For me, it was a trial and error approach. I have started in a big company as a consultant, only to realise that wasn’t for me. In time I realised an in-house position would suit me better. That’s why I went for a change and I will be starting in my new position next week. I’m happy and excited to experience how being an in-house data scientist will fit me.

If you don’t want to spend a couple of years of your life trying and erring as I did, here is a list of common types of positions for data scientists. You can find the link to the episode where I interview the guest with that position below the titles.

Consultant data scientist

Talking about data science in consulting with Madli Kivisik‍

Typical responsibilities: Being a consultant means that you will be working with clients of your employer. Your assignments are projects on client companies. You will be doing data science either in a team or alone in a different location/team than yours.

There are typical responsibilities of a consultant other than the technical data science tasks. These are mostly, helping with coming up with a business scope for the project, communicating with the client about what you’re doing and why it’s important, talking to business-people periodically to update them. It is a very “soft-skills” heavy position.

Projects: Projects are begun by salespeople of the consulting company going around pitching clients. You can get any sorts of projects. By this I mean, they can be in any industry. Think of the energy industry, hospitality, banking, finance, travel. Of course, the industry will depend on which companies your main employer is serving. It sounds interesting to have all the options but it is also likely that for every project you enjoy working on you might get assigned to a couple of projects that you won’t.

The projects might also be at any level. Even if you want to work on NLP, you might be stuck working on making dashboards months at a time. So make sure you understand what type of projects are done in a company before you start working there if you don’t want to be disappointed.
Career advancement: A consultant has both business and technical skills. So you have the option to grow into a managerial role or a more technical role.

Working hours and stress: Working hours can get challenging in consulting. You are not working directly for your employer but a client and consulting companies want to look good to their clients. This means that you might get extra pressure from your employer when it comes to deadlines.

What might have been a soft deadline in an in-house position, might become a hard deadline in a consulting environment just because your company promised the client to deliver at a certain time. This might cause a high-stress environment. But still, some companies do a good job managing this stress. Though I get the impression that working long hours and being fine with stress is “expected” of consultants.

Pros – projects in a variety of industries, mostly good pay, you get to learn the business side of things, actively using your soft-skills

Cons – you might get stuck with the type of projects you don’t like, working hours might be longer than average, the stress of working with a client

In-house data scientist

Becoming an ML engineer and work life at Twitter with Jigyasa Grover

Typical responsibilities: When you work as an in-house data scientist, you are solely interested in what’s going on inside your company. You will likely be in a team of data scientists and machine learning engineers. The projects will come to you from other teams inside the company. You likely will be in the same office (if that ever happens again that we return to the office) and that will be your main working spot. Apart from working on new projects, you are responsible for maintaining the projects you’ve delivered.

Projects: Projects are started by other teams in the company coming to your team with a request, your team coming up with an idea for a new feature or the need for an update to the currently-in-use systems.

Working hours and stress: Depends on the company. It’s likely that if the company is in a high-pace, high-pressure industry like finance/investment banking, you might work long hours and will be under stress. Though many companies are paying extra attention to not stressing their people out unnecessarily. Make sure you know what you’re signing up for before you start somewhere.

Career advancement: An obvious career path for an in-house data scientist is to become a team leader, manager or even CTO in the given company. The career path lies mostly on the technical side.

Pros – working in the same team, many opportunities to improve yourself especially if your colleagues are experienced

Cons – all your projects will be in the same industry (this might be a pro if you like working in that industry), apart from developing new projects you need to dedicate part of your time to maintaining old projects which might get tedious

Freelance data scientist

Freelancing and starting your own company with Katia Stambolieva

Typical responsibilities: The awesome thing about being a freelance data scientist is that your responsibilities are whatever you want them to be. You can choose to work for projects in the industries and the levels that you want. Mainly you would be developing projects with your clients, doing the technical work, presenting the results. Depending on what you agree on with your client, you might do less or more.

‍Projects: Projects happen by you finding clients and offering your services. You can find clients online, reaching out to your network or follow up on people/companies you know can use some data science support. It is likely that a seasoned freelance data scientist will have a clientele she/he repeatedly works with.

‍Working hours and stress: You can decide how much and how little you work as a freelance data scientist. This sounds like a dream but of course, it comes with a cost. The less you work, the less money you make. Especially if you’re just starting out, finding projects and clients could be a source of stress.

‍Career advancement: You can choose to join a company as a data scientist after having a successful career as a freelancer. Or you can decide to become a jewellery designer, or anything else. You have the freedom to choose your own path. Jokes aside though, you can grow your career by becoming an expert in a certain practice/industry or technology. (e.g. deep learning, conversation projects expert, NLP, image recognition etc.) So clients will know to come to you when they need help on these specific domains.

Pros – work whenever, where ever you want, choose the clients/projects you want to work with/on

‍Cons – the pressure of finding clients, the pressure of delivering to clients on time, you need to be the data scientist, accountant, salesperson, web developer and many other things in your own little enterprise

Researcher data scientist

Data Science in Research with Sakshi Mishra

Typical responsibilities: Out of all the people I’ve had on the podcast, researchers by far are having it the best in my opinion. But of course, it is not easy to get to where they are. Researchers mainly work towards a research goal and they deliver working prototypes and/or papers about the work they’ve done. They work on interesting projects that investigate new ideas and try to push forward today’s technology.

Projects: You can come up with your own research project or join on-going bigger projects. Of course, in order to do your own project, you need funding and that means you need to convince someone of the value of the project.

Working hours and stress: From what I’ve heard, researchers do have some stress over their work. When you’re working on a novel idea, there is a lot of uncertainty after all. You do not know if the project will succeed or if the work you put in will be used in real life. But other than that the working hours and the working environment is pretty relaxed.

Career advancement: If you work at an academic institution as a researcher you have the option to advance academically or join the industry. If you already work in industry or in an independent research lab, you can advance to take on more responsibilities.

Pros – very interesting work, the possibility of working on a passion project

Cons – might not be satisfying for people who want to see their work applied to real-life immediately

Free data scientist

Being a free data scientist with Yaakov Bressler

Typical responsibilities: There are not many responsibilities of a free data scientist. This is not a formal position but rather something you can do with your data science skills. A free data scientist would be contributing to open source projects, starting projects by him/herself or with some friends and trying new things and creating new projects out of thin air. It’s very fun to work this way if you don’t have to worry about money urgently. It will get you a lot of experience. And if you want it, make it easier for companies to trust your skills for when you want to be hired in the future.

Projects: Whatever you want to work on.

Working hours and stress: Again, you can work as much or as little as you want.

‍Career advancement: after contributing to open source projects and creating a couple of yours, companies might already come to you with offers. You can also start working as a freelance data scientist.

Pros – You have unlimited freedom to work on your own projects and spend your time as you will, great experience to show your capability and skills
Cons – Hard to make money this way

Positions that are not titled data scientist but are actively using data science

An unexpected way of using data science and life at Adobe with Shivali Goel

Typical responsibilities: This is a more general umbrella position. This can be anything from marketing to product development to customer relations. There are sometimes positions where people are not titled data scientist but their main tool is ML and data analysis and they effectively are doing data science. I’ve seen people take positions like that when they realised that their passion lies with the work and not with data science which is just a tool at the end of the day.

Career advancement: If you want to become a data scientist later in life, a position like this can be a great stepping stone in terms of experience but also in terms of understanding if you really want to be a data scientist.

Author: Mısra Turp

Mısra is a data scientist who helps busy professionals transform their careers into data science for a smooth transition. She writes articles and publishes free resources on her website for aspiring data scientists. She also hosts the podcast So you want to be a data scientist? where she interviews data professionals and talks about their journeys.

(Article originally published in soyouwanttobeadatascientist.com)

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The power of mentorship

Spela Hafner — Wed, 23 Sep 2020 14:43:04 +0000

How to help women in underrepresented groups to achieve their goals? What is your inner voice and how to find it? How important is coaching and mentoring towards shaping diversity&inclusion space in the companies and society?

Our special guest, Claudia Bruce-Quartey, who is Account Executive for MuleSoft, Strategic Networker and Motivational Coach, shared with us her experiences and knowledge on importance of mentoring and coaching to become better you as a person and as a leader.

Claudia graduated with a Master’s degree in Public Administration and Project Management from Universite Aix-Marseille-Université and earned her Bachelor’s in Political Science BA at the Leibniz University Hannover.

As a youth mentor and motivational coach, it has always been her aspiration to help people find their inner voice and light despite their current situation. Claudia believes in mentoring and her greatest reward is seeing the person in front of you as a much better version and taking honest steps towards their goals.

During our webinar we heard some great insights from Claudia on mentorship & coaching and how to become a great leader:

Q: How to find your inner voice?
A: Our inner voice presents our skills & powers: how can a person be more and do more to achieve goals. And this voice needs to be nurtured and mentored. Good mentor is able to help one find herself/himself as a person and find herself/himself as a leader. Be aware of what you are bringing to the table and nurture it.

Q: What is the difference between coaching and mentorship?
A: Coach can help you understand what your strenghts are and who you are. Mentorship can go beyond that: mentor can help you build a big picture about yourself and make you work harder for yourself. She/he shapes you and helps you understand what kind of player are you becoming on the field.

The responsibilities of outcome lies on mentee. Think about what you want out of the situation and go into it with the end goal in mind. Acknowledge where you stand in your life and match your mentor towards your goals.

Q: How can one see herself/himself as a leader?
A: When you impact the people and you change people’s lives, you are leading. If you want to become a good leader, you should ask yourself when leaving the room: what do I leave the audience with.
Leadership is a fluid journey: you have to nurture your skills and your emotions. If a situation makes you too emotional, take these steps: keep quiet, retreat, come back to get the power back over the situation.

For the conclusion Claudia gave great advice: The power belongs to you. If you cannot apply the power at one place, find another to apply it.

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Mentorship initiatives support Diversity & Inclusion

Spela Hafner — Thu, 17 Sep 2020 18:53:52 +0000

You can read everywhere about the importance of Diversity in organizations of all sizes. And when companies invest in Diversity & Inclusion to drive talent retention as much as to drive business outcomes, they are likely to be better performing, to be more agile and to be more successful.

It is difficult however to link Diversity & Inclusion initiatives to business outcomes and to measure their impact on revenues and profits. Though when employees believe in the commitment of their employer to diversity and feel included, according to Deloitte Research report^(*), innovation increases significantly.

So, how does Mentoring programs or Mentorship play and contribute to the Diversity & Inclusion initiatives and to the success of an organization?

Mentorship does foster a sense of belonging hence of inclusion: you are part of a broader program or initiative and you have a more neutral ally to help you move in the organization or develop your strengths.
Mentorship does help to expand one’s network: you reach out to new business areas, new department, you benefit from a more senior mentor who can help you get connections you might not find easy to make.
Mentorship does help to get the reassurance and the confidence that female workers more often seek: again here, you are not alone facing your questions or business problems to solve and you can discuss in a neutral and safe way.

In the past years, I have seen how important and essential a Diversity initiated Mentorship program was perceived and received, equally by our HR teams and by our employees. Not that business-driven Mentorship program would be less important, but clearly we realized that our open mentoring program cross business and geographies created for women with any type of experience and of all ages and regions did help them break the barriers of (1) networking, (2) career evolution and (3) reaching out of their comfort zone in a safe way and solving problems by providing informal relationships with a structured framework.

So, my recommendation is clearly to go for it, always and whenever you have a chance to join such a program! And never forget that you can always be a mentee and/or a mentor in a mentorship program. There is so much to learn from being either or!

Author: Marie-Amelie Masnou

Marie-Amelie has been leading the EMEA Women Impact Network (WIN) as well as the Germany WIN for many years while at HP. A flagship initiative of the Women Network supported by HP Global Diversity & Inclusion has been its Open Mentoring Program reaching out to hundreds of participants across businesses and spanning across many countries in EMEA for many years in a row.

^(*) Deloitte Research report

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The untapped potential of job sharing

Spela Hafner — Wed, 16 Sep 2020 17:33:53 +0000

Isabelle Litzler invited Maja to talk in her podcast Home Office about leveraging job sharing to develop Human Resource Effectiveness.

During this conversation, they discussed how companies can tap into the job sharing concept and increase their pool of talent and how to tap into the open opportunities of startup ecosystem in Switzerland.

You can listen to the podcast here:

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Interview: Part-time and job-sharing working models

Spela Hafner — Wed, 16 Sep 2020 16:54:37 +0000

Maja Schreiner gave two part interview in May 2020 to Rebeca Gelencser, a founder of a Career networking for women.

Rebeca invited Maja for an interview to learn more about part-time and job-sharing working models and to share the insights with her community.

Click on videos to hear both parts of the interview.

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Entrepreneurial DNA

Spela Hafner — Wed, 09 Sep 2020 20:48:42 +0000

What does it take to be a successful entrepreneur? Why are women less likely to believe they have what it takes to be entrepreneur?

Our guest speaker, Ingeborg Gasser-Kriss, who is a Senior Innovation Advisor, Board member @ SV Group and Alver World and Co-Director at Founder Institute Zurich, shared with us interesting insights into characteristics that correspond positively entrepreneurial success.

These are the main traits:

1. Professional Experience = be a master of your domain.
2. High Fluid Intelligence = be agile and quick to change; don’t be attached to what you think – mental agility.
3. High Openness = seek experiences, knowledge, change; be open to new things and to feedback.
4. Moderate Agreeableness = be able to balance collaboration with conflicts.

We also learned main reasons, why do startups fail:

1. The founder gives up.
2. Team that doesn’t work together.
3. Company not growing fast enough OR growing too fast.

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How to establish an Employee Resource Group

Spela Hafner — Tue, 08 Sep 2020 19:36:07 +0000

Employee Resource Groups (ERGs) are voluntary, employee-led groups that foster a diverse, inclusive workplace, aligned with organizational mission, values, goals, business practices, and objectives. Other benefits include the development of future leaders, increased employee engagement, and expanded marketplace reach.

We invited Ruth Wood, Product Marketing Manager at Beekeeper, to speak about her experience setting up a diversity, equity and inclusion ERG at work and share some best practices.

We highlighted some key factors to support DEI within the company and start establishing ERG at workplace:

1. Start talking with your peers on the breaks, at the coffee machine etc. Ask questions on diversity & inclusion, find informations & best practices and share your knowledge.

2. Get more focused on the topic. Run a survey within the company to get insights, how important is this topic for your colleagues and get their opinion.

3. Represent the results to the top level and start initiatives. Write content, organize workshops, do the research, run a survey, etc. = ENGAGE.

Think about what you can do to initiate DEI conversations, create a business case and invite leading team to the discussion.

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Tech Face podcast with Maja Schreiner

Spela Hafner — Thu, 03 Sep 2020 21:23:45 +0000

Priska Burkard from TechFace invited Maja to talk in TechFace podcast and share her experience in job sharing in Tech.

Maja explained when she became aware of how important it is to be able to work part-time and this led her to found her own business – enabling companies to offer job-sharing and part-time working opportunities to employees in tech to drive better balance between work time and private time.

Maja shared her wide experiences working in tech industry and her expertise in job-sharing.

Listen to the podcast and find out, who is Maja’s favorite female role model.

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Maja Schreiner’s interview in Fearless Fridays: From corporate life to helping women in tech

Spela Hafner — Thu, 03 Sep 2020 20:19:18 +0000

Anna S.E. Lundberg from One Step Outside talked with Maja on career transition in Fearless Fridays interview.

They talked about shared belief that employers should encourage, not hinder, their employees who want to pursue an interesting project alongside their work. This has benefits for the employee, for their job satisfaction and therefore retention, as well as having potential positive reverberations on the company and its work.

They also talked about the importance of working through any of those “what ifs” that we all have BEFORE we take the leap, ensuring that we have the support of our nearest and dearest, and getting expert guidance from a mentor or accelerator to get your business to where you want it to be.

Some excerpts from the interview:

Maja on:

biggest challenge when starting her own business

“The most important thing is to know that at the beginning, you might not be earning money with your business. You have to be quite clear what your budget is and for what time.”

“And what it also turned out to be is it’s even more than the full-time job. It’s almost like a 24/7 job.”

“Be clear about your budget, about your time, about your work-life balance and then be ready to do all those different things you might have not been doing full time before.”

support from leadership team

“For all bosses in the corporates, please do allow your ladies and your employees to do projects on the side because you never know how huge this project will get and what kind of impact they might even have on your enterprise, positive impact.”

best part of doing your own business

“Product development, even improving the design, getting all these different feedbacks and then last but not least making a decision because you might get all different feedbacks, regarding all different topics.”

You can watch the whole interview here:

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Job-Sharing in Data Science

Spela Hafner — Thu, 03 Sep 2020 19:47:48 +0000

On our 10th anniversary webinar in July 2020 we have broaden the focus in skills-building around data science and data analysis.

Our community member Olena Bugaiova, who is a Data Science student at MITx, introduced the audience to the basics of data science and explained what activities are ideal to be done in the job-sharing model. Olena talked about:

1. What is Data Science

2. Domain Areas for Data Science / Data Analysis

3. Data Science Project Life Cycle

Identifying the problem
Collecting data
Preparing the data for analysis
Building a Machine Learning model
Presenting the model
Deploying the model for a general case

4. Thoughts on how the process can be split for job-sharing. -> You can see the orange highlighted phases on the below diagram.

We rounded the webinar with a Q & A.

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