#datacareer - Analyst? Engineer? Scientist? Roles in industry and startups with special guest Andrew Emmett

9th edition of the live webinar series supporting you 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/n8Cl2PomjPY

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 Andrew Emmett
Andrew Emmett is a Data Analyst with a background in business intelligence and business analytics. He was the Head of Analytics at FreeNow, becoming the Head Coach Data Analytics at Neuefische. Andrew has created information tools that allow decisions to be made more quickly, with more certainty and less effort to create value for businesses and customers.

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.

Dr. Irena Bojarovska is a Data Scientist in Marketing at Zalando SE. She holds a PhD 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

1. #DATACAREER Analyst? Engineer? Scientist? DATA ROLES IN THE INDUSTRY AND STARTUPS 9th edition with Andrew Emmet
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. Accredited practitioner with 5+ years of experience Validated expertise in Data Analytics and Data Science in Marketing LinkedIn Dr. Irena Bojarovska
4. Head of #datalift and founding member at AI Guild ML models for predictive analytics Former bootcamp teacher #datacareer since 2012 LinkedIn DÂNIA MEIRA
5. Andrew Emmett Head Coach Data Analytics Launched and led BI team, and then became Head of Analytics Masters in Logistics and Supply Chain Management LinkedIn
6. #DATACAREER WORKSHOP OUTLINE AI Guild advancing careers #datacareer orientation #datacareer roles #datacareer journey
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
8. Specialization and differentiation of roles Rising value of domain expertise Experimental phase with PoC plays ending Increasing focus on deployment OBSERVING THE MARKET
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.
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
11. #DATACAREER WORKSHOP OUTLINE #datacareer orientation AI Guild advancing careers #datacareer roles #datacareer journey
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
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
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
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
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
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
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
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
20. #DATACAREER WORKSHOP OUTLINE #datacareer roles #datacareer journey #datacareer orientation AI Guild advancing careers
21. ANDREW EMMETT - EARLY CAREER 1998 2002 Economics B.A. Economic History, Econometrics Maritime Industry Customer Service, Digitisation Projects 2003 2007
23. ANDREW EMMETT - DATA PEOPLE Business Understanding Data Wrangling Math / Stats Communication Project Management
24. ANDREW ON STARTING A CAREER AS DATA ANALYST
25. #DATACAREER WORKSHOP OUTLINE #datacareer orientation AI Guild advancing careers #datacareer roles #datacareer journey
26. MARKET ORIENTATION
27. WHICH INDUSTRIES ARE WINNING BIG?
28. 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
29. WHICH GOALS ARE EFFICIENTLY ACHIEVED WITH AI?
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
31. GOALS OF DATA ROLES
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
33. SOME STARTUP CHALLENGES • Data volume, accessibility, and quality • Company funding and runway • Expertise levels and team size
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
35. #DATACAREER - QUALITY STANDARDS
37. Join at theguild.ai/community THANK YOU! www.theguild.ai