#datacareer - Analyst? Engineer? Scientist? Roles in industry and startups with special guest Dr. Eva Martínez Romero

10th edition of the live webinar series supporting you to find your next role with insights from Europe's leading AI practitioner network.

How to find the best 2nd and 3rd data role in a rapidly evolving and increasingly specialized data economy? Enhance your career with insights from the AI Guild.

Join the next event live: https://www.eventbrite.de/o/ai-guild-27115216103

Watch the recorded live session here: https://youtu.be/k5tRqB5TQuw

This workshop offers you the following:
* How you find the right role among the specialized roles in e.g. data engineering, data analytics, data science, machine learning, deep learning, computer vision, and natural language processing.
* Q&A for your most important questions as practitioners.
* Key insights from the AI Guild on the labor market, hiring, and career development.

Our special guest is Dr. Eva Martínez Romero
Eva Martínez is Data and Analytics Manager of the Product Analytics team in Contentful. She discovered her passion for Data Analytics around 5 years ago, while finishing her PhD in mathematics. She has gone through a journey involving end to end data analytics in different contexts, always seeking for having a great impact. Today she mixes her passion for Math & Product Analytics to lead her team of product analysts to provide actionable insights for effective decision making & data evangelization.

Your hosts
Dânia Meira is a Senior expert and mathematician in the data field since 2012 with a Data Science career in Berlin startups where her work focused on ML for predictive analytics. She is also an experienced teacher and mentor. Dânia is the head of #datalift and also a Founding member of the AI Guild.

Dr. Irena Bojarovska is a Data Scientist in Marketing at Zalando SE. She holds a Ph.D. in Mathematics and started her career in Data Analytics. Irena is an AI Guild accredited expert qualified to perform her profession at the highest standards for technical competence, ethical behavior, business impact, and benefits to society.

Transcript

2. #DATACAREER “No matter who you are, self-improvement is one of the most important and most overlooked attributes of young AI talent. It only takes four years of experience to become a senior AI researcher, or five years of experience to lead an entire institute. The determination and discipline to improve both the hard and soft skills continually will be the deciding factor in an AI researcher’s career.” Jean-François Gagné 2
3. Accredited practitioner with 5+ years of experience Validated expertise in Data Analytics and Data Science in Marketing LinkedIn Dr. Irena Bojarovska 3
4. Head of #datalift and founding member at AI Guild ML models for predictive analytics Former bootcamp teacher #datacareer since 2012 LinkedIn Dânia Meira 4
5. Dr. Eva Martínez Romero Data and Analytics Manager 4+ years of experience in Data Analytics PhD in Mathematics LinkedIn 5
6. #DATACAREER WORKSHOP OUTLINE AI Guild advancing careers #datacareer orientation #datacareer roles #datacareer journey 6
7. Search for the 1st as well as the 2nd role may take >6 months Upgrading inside a company may be easier Job advertisements may be misleading and confusing The role ‘in real life’ may not match the talents expectations LEARNINGS FROM ADVANCING CAREERS 7
8. Specialization and differentiation of roles Rising value of domain expertise Experimental phase with PoC plays ending Increasing focus on deployment OBSERVING THE MARKET 8
9. THE AI GUILD HAS YOU COVERED FROM ENTRY-LEVEL TO LEAD AND CxO www.datacareer.eu 100+ practitioners have joined for career coaching and the development program. Together, we are building the career paths and establishing quality standards. 9
10. OPPORTUNITIES FOR AI GUILD MEMBERS Host an event Career development program Accredited expert Be the special guest like Irena Bojarovska, Adam Green, Ellen König, Alexey Grigorev, Tereza Iofciu, Lisa Heße, Paul Elvers, Macarena Beigier-Bompadre, Patrick Baier, Marija Vlajic Wheeler. like Dânia Meira, Macarena Beigier-Bompadre, Chris Armbruster, Dina Deifallah, Fahrnaz Jayrannejad, Sahar Hashai, Rachel Berryman, Marija Vlajic Wheeler, Filipe Conceição, Ana Chubinidze. like Yann Lemonnier, Sara Rarís Miralles, Promit Ray, Johanna Viktor, Sandra Yojana Meneses, Gelavizh Ahmadi, Andrés Prada González, Lisa Heße, Fahrnaz Jayrannejad, Eva Jaumann, Verena Gorris, Aline Quadros. like Irena Bojarovska, Sébastien Foucaud, Tereza Iofciu, Mirko Knaak, Nour Karessli, Florian Baumann, Irina Vidal Migallón, Nicholas Hoff, Darina Goldin, Tristan Behrens, Martin Huber, Kristian Rother. Find out more on datacareer.eu 10
11. #DATACAREER WORKSHOP OUTLINE #datacareer orientation AI Guild advancing careers #datacareer roles #datacareer journey 11
12. PRODUCTIONIZING MACHINE LEARNING ML Models Data Collection Data Quality Infrastructure Process Management Tools Monitoring Feature Extraction Analysis Data Preprocessing Parameter Configuration Offline Validation A/B Testing Data Engineer Data Scientist Data Analyst ML Engineer AI Researcher Data roles See also: “Hidden Technical Debt in Machine Learning System” by Sculley et al, Google inc, 2015 Machine Resource Management Configuration Business Logic 12
13. #DATACAREER ROLES Task Understand business case, build features to train predictive models to address such use cases Skill Statistics, SQL, programming (e.g. python, R), ML & DL techniques. Data Scientist Task Business and data understanding to report on what happens Skill Descriptive analytics, SQL, statistics, dashboarding and visualization tools Data Analyst Data Engineer Task Build and maintain infrastructure and pipeline to collect, clean and pre-process data Skill Distributed systems, databases, software engineering Task Optimize, deploy and maintain machine learning models in production Skill Software engineering, devOps and systems architecture Machine Learning Engineer Task Build new machine learning algorithms, find custom scientific solutions Skill Research, presenting at conferences, writing publications AI Researcher 13
14. • The full picture of deploying a solution needs a variety of skills, uncommon to acquire by a single person. • All the skill sets needed for successful execution need to collaborate • Team => Complementing expertise of one another • Team members => understand the full picture of end-to-end ML will be helpful in developing work in a more organized way, and consolidating it more efficiently Others / new roles (...?) Domain Expert DATA SCIENCE IS A TEAM SPORT Data Engineer Data Scientist Data Analyst ML Engineer AI Researcher Data roles Product Owner DevOps Engineer CV Engineer NLP Enginner Product and Business roles Tech expert roles 14
15. D e e p Broad ML Algorithms Visualization Domain Expertise Programming SW Engineering Communication Tools Platforms Statistics T-SHAPED SKILL SETS FOR DATA ROLES Data Engineer Data Scientist Data Analyst ML Engineer AI Researcher 15
16. Data Analyst Descriptive statistics Hypothesis testing Probability distributions Regression & Classification Excel Tableau (...) + Data interpretation Logical approach SQL R and/or Python Marketing Healthcare E-commerce Mobility Manufacturing (...) ML Algorithms Visualization Domain Expertise Programming SW Engineering Communication Tools Platforms Statistics SKILL SETS FOR DATA ROLES 16
17. Data Scientist SQL R and/or Python + JupyterLab Git (...) Marketing Healthcare E-commerce Mobility Manufacturing (…) Data interpretation Logical approach pandas, scikit-learn, numpy, keras (...) + Probability distributions Regression & Classification Deep Learning ML Algorithms Visualization Domain Expertise Programming SW Engineering Communication Tools Platforms Statistics SKILL SETS FOR DATA ROLES 17
18. Hadoop Databases Git, Docker, Airflow, Jenkins SQL, Bash, Java, Scala, Python Data pipelines Data structures Linux, AWS, Google Cloud Platform, Microsoft Azure Data Engineer ML Algorithms Visualization Domain Expertise Programming SW Engineering Communication Tools Platforms Statistics SKILL SETS FOR DATA ROLES 18
19. ML Engineer scikit-learn Microservices Infrastructure Linux, AWS, Google Cloud Platform, Microsoft Azure Hadoop Databases Git, Docker, Airflow, Jenkins SQL, Bash, Java, Scala, Python ML Algorithms Visualization Domain Expertise Programming SW Engineering Communication Tools Platforms Statistics SKILL SETS FOR DATA ROLES 19
20. #DATACAREER WORKSHOP OUTLINE #datacareer roles #datacareer journey #datacareer orientation AI Guild advancing careers 20
21. 21 Product Analytics Full-time working mum Data and Analytics Manager Bachelor, Master & PhD in Mathematics Consultant, Biostatistician for Clinical Research BI (data engineering) ...
22. My first year as Data & Analytics Manager 🎂 22 Scrum Master Product Manager Keeping Tech skills up to date • Personal Development • Expectation Management • Mentoring • Recruitment • Team building • Enabler • Defining internal & external processes • Ceremony master • Ensuring Productivity • Stakeholder management • Developing Vision / Strategy • Prioritization • Roadmap planning • Discovering new analytical projects My role People Manager
23. My first year as Data & Analytics Manager 🎂 23 Data Engineers + Data Analysts Evolution of the team Data Engineers • Data Modelling (ETL/ELT) • Product Analytics • Company-wide projects Data Engineers + Analytics Engineers • Product Analytics • Company-wide projects • Product Analytics Data Analysts Data Analysts Product Analysts Research Product Analysts Data Analysts Now 2021 2020 2020-09
24. What is important for me? 🔍 24 1. The product - What is the product I will be contributing to? 2. The company culture and values - Do the company’s values align with mine? - Will the company culture fit my personality? 3. The role - How does it align with my career development? - Will I be challenged in a positive way? 4. The team - Are the team members people I’d love to work with? - How will your manager set me for success? Will they make me feel appreciated? 5. The offer - Competitive salary and benefits
25. #DATACAREER WORKSHOP OUTLINE #datacareer orientation AI Guild advancing careers #datacareer roles #datacareer journey 25
26. MARKET ORIENTATION 26
27. IN WHICH AREAS IS AI USED IN THE INDUSTRIES? Source: State of AI in the Enterprise – 3rd Edition, Deloitte, 2020 I Ergebnisse der Befragung von 200 AI-Experten zu Künstlicher Intelligenz in deutschen Unternehmen 27
28. WHICH GOALS ARE EFFICIENTLY ACHIEVED WITH AI? 28
29. WHICH INDUSTRIES ARE WINNING BIG? 29
30. A SHORTAGE OF SKILLED EXPERTS STILL A FACTOR* ◼ 27% of the German companies surveyed describe the shortage of specialists as one of the biggest hurdles in the implementation of AI projects ◼ On a global level, other topics are more being a barrier *State of AI in the Enterprise – 3rd Edition | Ergebnisse der Befragung von 200 AI-Experten zu Künstlicher Intelligenz in deutschen Unternehmen, 2020 30
31. GOALS OF DATA ROLES 31
32. KEY INDUSTRY CHALLENGES* ◼ Data volume, accessibility, and quality ◼ Trust of customers, stakeholders, and employees, including governance, compliance, and reputation ◼ Competence of employees, management, and company *Based on the 2019 PWC report “Künstliche Intelligenz in Unternehmen”, p. 12 32
33. SOME STARTUP CHALLENGES • Data volume, accessibility, and quality • Company funding and runway • Expertise levels and team size 33
34. WRAPPING UP Keep observing the market Look for matches between employers’ needs and your skills profile Scan the industry and startups for the most promising #aiusecase 34
35. #DATACAREER - QUALITY STANDARDS 35
37. Join at theguild.ai/community THANK YOU! www.theguild.ai 37