#datacareer - Analyst? Engineer? Scientist? Roles in industry and startups with data scientist Irena Bojarovska

“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 DATA SCIENTIST IRENA BOJAROVSKA
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. Founding member, AI Guild 10,000 Data Scientists for Europe Former bootcamp director #datacareer coaching since 2017 LinkedIn CHRIS ARMBRUSTER
4. Founding member, AI Guild ML models for predictive analytics Former bootcamp teacher #datacareer since 2012 LinkedIn DÂNIA MEIRA
5. IRENA BOJAROVSKA One of the first AI Guild members Juggling data at Air Berlin for 1.5 years Since 2017 at Zalando’s Marketing Data & Analytics department PhD in Applied Mathematics Mother of two daughters LinkedIn
6. #DATACAREER WORKSHOP OUTLINE AI Guild advancing careers #dataroles specialization #dataroles skills #datacareer orientation
7. AI Guild @ai_guild AI Guild AI Guild www.theguild.ai AI Guild
8. AI GUILD ADVANCING CAREERS The Career Development Program For practitioners, by practitioners: Experience how the AI Guild is working from practitioners' experience to make predictable your progress as a data professional to the senior level For professionals with at least 1 year of experience Data Roles Analyst? Engineer? Scientist? Science to Data Science Supporting PhDs and postdocs interested in a #datacareer CV clinic Take the chance to review your CV and your online profile Getting the job offer #datacareer search, coding challenge, and interview Members-only Members-only
9. 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
10. Specialization and differentiation of roles Rising value of domain expertise Experimental phase with PoC plays ending Increasing focus on deployment OBSERVING THE MARKET
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. ‚COOKING‘ DATA: EXPLAINING SPECIALIZATION ML Models Data Collection Data Quality Infrastructure Process Management Tools Machine Resource Management Monitoring Configuration Feature Extraction Analysis Data Preprocessing Parameter Configuration Offline Validation Business Logic A/B Testing See also: Understanding a Machine Learning Workflow Through Food by Daniel Godoy Sowing Harvesting Choose recipe Prepare ingredients Customers tasting Kitchen Tasting Use utensils Try combinations of appliances and recipes Kitchen space and available appliances
15. UNDERSTANDING #DATAROLES Build Kitchen Appliances Create and use recipes to cook Check quality of ingredients and recipes Process ingredients at scale Turn a recipe into many dishes served efficiently Data Engineer Data Scientist Data Analyst ML Engineer AI Researcher
16. #DATACAREER WORKSHOP OUTLINE #dataroles specialization #dataroles skills #datacareer orientation AI Guild advancing careers
17. DS at Zalando PhD at TU Berlin Gymnasium in Macedonia 2002 2006 2012 2016 2017 BsC & MsC in Russia Analyst at Air Berlin IRENA BOJAROVSKA - TIMELINE
18. D e e p Broad ML Algorithms Visualization Domain Expertise Programming SW Engineering Communication Tools Platforms Statistics SKILL SETS FOR DATA ROLES Data Engineer Data Scientist Data Analyst ML Engineer AI Researcher
19. 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
20. 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
21. Data Analyst BsC can be sufficient Presenting results (historical data) as facts Few and easy to learn Data Scientist PhD is often common Presenting predictions based on complex models SE knowledge and fast learning BACKGROUND INTERESTS SKILLS ↯ often it could be still called Data Analyst WHICH ONE IS FOR YOU, BASED ON YOUR…
22. MORE OF.. Software engineering skills Mathematical and statistical knowledge Conferences LESS OF.. Direct participation in business decisions Stakeholder management Manual work REMAINS.. The passion for data The troubles of data The ability to help make data-based decisions TRANSITIONING FROM ANALYST TO SCIENTIST REQUIRES...
23. IRENA ON TRANSITIONING FROM ANALYST TO SCIENTIST Moving internally is easier Automated reporting leaves space for learning new skills Being active in the community opens new doors Constant learning is essential
24. 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
25. 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
26. #DATACAREER WORKSHOP OUTLINE #dataroles specialization #dataroles skills #datacareer orientation AI Guild advancing careers
27. MARKET ORIENTATION
28. WINNING BIG AND SMALL
29. 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
30. SOME STARTUP CHALLENGES • Data volume, accessibility, and quality • Company funding and runway • Expertise levels and team size
31. 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
32. Join at theguild.ai/community THANK YOU! www.theguild.ai