#Datacaeer - AI Guild workshop on data roles in industry with Adam Green
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Based on AI Guild career coaching this workshop looks at roles such as Data Analyst, Data Scientist, and Data Engineer in industry and startups. We discuss emerging specialization, and how to upgrade your competence profile. Also included, tips and tricks from practitioners on how to find your next role. Please find the event series on aiguild.eventbrite.com
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#Datacaeer - AI Guild workshop on data roles in industry with Adam Green
- 1. #DATACAREER FIRST AND SECOND #DATAROLES IN THE INDUSTRY AND STARTUPS SPECIAL EDITION WITH ADAM GREEN FROM AIGUILD.EVENTBRITE.COM
- 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
- 4. Dânia Meira Founding member, AI Guild ML models for predictive analytics Former bootcamp teacher #datacareer since 2012 LinkedIn
- 5. Adam Green Founding member, AI Guild Senior data scientist Former bootcamp director Focus on energy industry LinkedIn
- 6. Chris Armbruster Founding member, AI Guild 10,000 Data Scientists for Europe Former bootcamp director #datacareer coaching since 2017 LinkedIn
- 9. AI GUILD CAREER COACHING Running for junior and for senior practitioners since early 2019 Runs monthly for AI Guild members Coaching capacity per year: 240 participants
- 10. INSIGHTS FROM CAREER COACHING 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
- 11. OBSERVING THE MARKET Specialization and differentiation of roles Rising value of domain expertise Experimental phase with PoC plays ending Increasing focus on deployment
- 12. ANECDOTAL EVIDENCE FOR DIGITAL ADOPTION AND BEHAVIOR INCREASING 10X … but labor market admittedly very difficult
- 13. #DATACAREER WORKSHOP OUTLINE #dataroles specialization #dataroles upgrading #datacareer orientation
- 14. PRODUCTIONIZING MACHINE LEARNING 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 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
- 15. #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 under- standing to report on what happens Skill Descriptive analytics, SQL, statistics, dashboarding and visualization tools Data Analyst Data Engineer Task Build and maintain infra- structure 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
- 16. ‚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
- 17. 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
- 18. ADAM’S CASE: COMING INTO DATA FROM INDUSTRY Chemical engineering and black box modelling Working as energy engineer with spreadsheets and linear programming From bootcamp graduate to director
- 19. ADAM’S TAKE ON #DATAROLES THE DATA ANALYST ENRICHES DATA THE DATA SCIENTIST MAKES PREDICTIONS THE DATA ENGINEER ENABLES ACCESS TO DAATA
- 20. WHAT IS GREAT ABOUT BEING A DATA SCIENTIST ¡ A never-ending story of learning ¡ Tooling is free ¡ Lots of freely accessible data ¡ Leverage of the technology ¡ The variety of non-traditional and interdisciplinary routes into the field ¡ Future proof ¡ People are excited and interested in what you do ¡ Many interesting life lessons
- 21. WHAT IS GETTING EASIER ¡ Tooling ¡ Putting code into production ¡ Differentiation of roles
- 22. WHAT IS STILL DIFFICULT ¡ Knowing where to stop learning ¡ Mastering new algorithms ¡ Keeping up with research ¡ Dealing with the impostor syndrome ¡ Access to simulators ¡ APIs and libraries for Reinforcement Learning
- 23. #DATACAREER WORKSHOP OUTLINE #dataroles upgrading #datacareer orientation
- 24. LET‘S START FROM YOUR ‚USERS‘ AND ‚CUSTOMERS‘ ¡ Hiring managers ¡ Human resources ¡ Recruiters ¡ Network of friends and colleagues ¡ Company leaders
- 25. DATA ENGINEER SQL, Bash, Java, Scala, Python Hadoop: Hive, Pig, Spark Databases e.g. Microsoft SQL, PostgreSQL, MongoDB Platforms: AWS, Google Cloud Platform, Microsoft Azure, Linux Tools: git, docker, airflow, Jenkins Language specific skills are important, also for ETL and databases. Certifications with AWS, Google, or Cloudera may be relevant. Key topics include data pipelines, algorithms and data structures, and the understanding of system design.
- 26. DATA ANALYST SQL, Excel Visualization tools like Tableau Python/R packages like matplotlib, seaborn, ggplot2 Key topics include statistical knowledge, data analysis, data interpretation, and logical approach.
- 27. DATA SCIENTIST SQL, Bash R: dplyr, sqldf, tidyr, lubridate, shiny, ggplot2, MLR, ranger, xgboost Python: numpy, pandas, matplotlib, scikit-learn, keras, Hadoop: Hive, Pig, Spark Databases: Microsoft SQL, PostgreSQL, MongoDB Tools: git, jupyter notebook, docker Models & algorithms: Statistical models and distributions, linear and logistic regression, random forest, backpropagation, ARIMA, Natural Language Processing, Computer Vision
- 28. WHERE ARE WE TODAY? ML IS WIDELY DEPLOYED AND THE PRACTICE DEVELOPING CREATIVELY MORE AND MORE INDUSTRIES ARE PROGRESSING FROM DIGITAL TO DATA AND ARTIFICIAL INTELLIGENCE VALIDATING THE BUSINESS CASE IS KEY
- 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
- 36. …AMONG EARLY AI ADOPTERS IN THE UNITED STATES
- 41. …AMONG EARLY ADOPTERS IN THE USA
- 44. 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