#datacareer - Analyst? Engineer? Scientist? Roles in industry and startups with head data science coach Tereza Iofciu

“The problem for AI in Europe is not the money, it is finding the talent” (Leading European AI practitioner)

Data and Artificial Intelligence constitute the fastest-growing job market for the highly qualified. This workshop offers you the following:

- How to find the right role for you among the emerging specialized roles in e.g. data engineering, data analytics, data science, machine learning, and deep learning.
- Pragmatic advice on handling your CV and skills profile for your next role.
- Orientation on the labour market, what employers miss most, and which #aiusecase are winning.

Transcript

1. #DATACAREER DATA ROLES IN THE INDUSTRY AND STARTUPS SPECIAL EDITION WITH HEAD DATA SCIENCE COACH TEREZA IOFCIU
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
3. AI Guild @ai_guild AI Guild AI Guild www.theguild.ai AI Guild
4. Founding member, AI Guild 10,000 Data Scientists for Europe Former bootcamp director #datacareer coaching since 2017 LinkedIn CHRIS ARMBRUSTER
5. Founding member, AI Guild ML models for predictive analytics Former bootcamp teacher #datacareer since 2012 LinkedIn DÂNIA MEIRA
6. TEREZA IOFCIU Data Science manager 15+ years of #datacareer Skilled in Data Science, Data Engineering and Product Management PhD focused in Information Retrieval LinkedIn TEREZA’S PICTURE
7. #DATACAREER WORKSHOP OUTLINE AI Guild advancing careers #dataroles specialization #dataroles skills #datacareer orientation
8. 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
9. Specialization and differentiation of roles Rising value of domain expertise Experimental phase with PoC plays ending Increasing focus on deployment OBSERVING THE MARKET
10. OPPORTUNITIES FOR 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 Corrie Bartelheimer, Yann Lemonnier, Sara Rarís Miralles, Promit Ray, Johanna Viktor, Sandra Yojana Meneses, Hannes Müllner, Gelavizh Ahmadi, Andrés Prada González, Lisa Heße, Fahrnaz Jayrannejad, Eva Jaumann, Verena Gorris, Dr. Aline Quadros, Jessica Franks (and lead by Marija Vlajic Wheeler). live in March 2021, find out more on theguild.ai
11. #DATACAREER WORKSHOP OUTLINE #dataroles specialization #dataroles skills #datacareer orientation AI Guild advancing careers
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 #dataroles See also: “Hidden Technical Debt in Machine Learning System” by Sculley et al, Google inc, 2015 Machine Resource Management Configuration Business Logic
13. #DATAROLES 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
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 “DATA SCIENCE IS A TEAM SPORT” Data Engineer Data Scientist Data Analyst ML Engineer AI Researcher #dataroles Product Owner DevOps Others (..?)
15. #DATACAREER WORKSHOP OUTLINE #dataroles specialization #dataroles skills #datacareer orientation AI Guild advancing careers
16. TEREZA IOFCIU - the old school path to data science Studied computer science (Bucharest) Diplom Arbeit and PhD at L3S (Hannover) Senior Perl Developer @ Xing .. aka Data Science BI Dev @mytaxi .. aka Data Eng -> DS -> .. Head Data Science Coach at neuefische 2000 2005 2008 - data science term is coined 2011 2016 2020
17. TEREZA IOFCIU - when did my career growth start? 2000 - 2015 - going with the flow - Leading to my first mini burnout - 2015 2016 -2021 - started taking responsibility and driving change - Leading to my second mini burnout in 2020
18. TEREZA IOFCIU - taking responsibility of your career “Periodically write down what your dream job would be” WHY: - Perspective - Reflective - Evolving - Development - Learning - Initiative
19. TEREZA IOFCIU - redefining your career - example My roles in the past 5 years: - Data engineer - Data scientist - Agile coach - Product manager - Psychiatrist / GLUE - Senior data scientist - Data science lead - Data science coach
20. 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
21. 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
22. 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
23. 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
24. 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
25. #DATACAREER WORKSHOP OUTLINE #dataroles specialization #dataroles skills #datacareer orientation AI Guild advancing careers
26. MARKET ORIENTATION
27. WINNING BIG AND SMALL
28. 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
29. SOME STARTUP CHALLENGES • Data volume, accessibility, and quality • Company funding and runway • Expertise levels and team size
30. 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
31. AI GUILD ADVANCING CAREERS 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.
32. Join at theguild.ai/community THANK YOU! www.theguild.ai