#datacareer - Analyst? Engineer? Scientist? Roles in industry and startups with special guest Johanna Viktor

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://www.youtube.com/watch?v=RzpB18b_X9Y

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 labor market, what employers miss most, and which #aisusecase are winning.

Our special guest is Johanna Viktor
Johanna is a Data Scientist with an engineering background working for industrial corporates for 8+ years. Her focus is on using ML to drive projects for efficiency gains. She is experienced in running large international projects preparing data infrastructures and delivering proof-of-concept. She is fluent in Chinese, English, and German.

Target participants
Talents looking for a 2nd or 3rd role in the field. You typically will have some experience already - and possibly up to 4 years in the field.

If you are looking for a first role you are likely to enjoy analytical thinking and have experience in Python or R and the relevant libraries. Also, if you are looking for a career change, then your background may well be in e.g. related data professions (like BI) or software engineering, or a data-intensive Ph.D. in a STEM discipline.

Proof-of-concept
- 1000+ high-potential talents converted to data careers by AI Guild members as e.g. company trainers, online mentors, bootcamp instructors, hiring managers.
- 150+ AI Guild leaders in the field, e.g. bootcamp directors, university professors, technical leads, business architects, startup founders, industry CxO.
- The AI Guild community with 800+ members.

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.

Chris Armbruster designed and ran logfile analysis as the web was scaling (2008-2012); is an ex-bootcamp director, and runs the campaign 10,000 Data Scientists for Europe. Chris is the head of #datacareer and also a Founding member of the AI Guild.

Transcript

1. #DATACAREER DATA ROLES IN THE INDUSTRY AND STARTUPS SPECIAL EDITION WITH SENIOR AI SPECIALIST JOHANNA VIKTOR
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. 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.
4. Head of #datacareer and founding member at AI Guild 10,000 Data Scientists for Europe Former bootcamp director #datacareer coaching since 2017 LinkedIn CHRIS ARMBRUSTER
5. Head of #datalift and founding member at AI Guild ML models for predictive analytics Former bootcamp teacher #datacareer since 2012 LinkedIn DÂNIA MEIRA
6. JOHANNA VIKTOR Working for industrial corporates for 8+ years ML for efficiency gains Running large international projects Fluent in Chinese, English, and German LinkedIn
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 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. live since March 2021, find out more on datacareer.eu
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 Others / new roles (...?) Domain Expert DATA SCIENCE IS A TEAM SPORT Data Engineer Data Scientist Data Analyst ML Engineer AI Researcher #dataroles Product Owner DevOps Engineer CV Engineer NLP Enginner Product and Business roles Tech expert roles
15. #DATACAREER WORKSHOP OUTLINE #dataroles specialization #dataroles skills #datacareer orientation AI Guild advancing careers
18. 18 House of AI Guiding Theme Operationalization of the DB AI strategy Center of Excellence for Data Usage and AI in DB Motivation House of AI as part of Data Intelligence Center coordinates existing expertise and creates the basis for integrating AI into processes of DB. Feasibility to product Project developmen t AI Network Inner Source Community of Practice AI Best practices & standards Develope overall AI processes Collaborativ e implementat ion in subsidiary companies Part of Strong Rail Strategy Governance Implementation Central technology of D&T strategy Common Resource Manageme nt Technical AI architecutre TDD - House of AI | JR East Cooperation | Data & AI Strategy and Use Cases | March 2021
19. 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
20. 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
21. 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
22. 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
23. 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
24. #DATACAREER WORKSHOP OUTLINE #dataroles specialization #dataroles skills #datacareer orientation AI Guild advancing careers
25. MARKET ORIENTATION
26. WINNING BIG AND SMALL
27. 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
28. SOME STARTUP CHALLENGES • Data volume, accessibility, and quality • Company funding and runway • Expertise levels and team size
29. 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
30. AI Guild @ai_guild AI Guild AI Guild www.theguild.ai AI Guild
31. Join at theguild.ai/community THANK YOU! www.theguild.ai