![Org State and role mapping
Decision workflows, Business
Process Integrations
Java, Python, Spark,
Apache Beam, GCP
Dataflow
Data Pipeline / DW
R, Python Visualization, D3.js
Hive, BigQuery
Data Analysis
Collect Store Explore Operationalize Automate Predict/Understand Optimize
CHIED DATA OFFICER, DATA RISK ANALYST,
EXPERT ANALYTICS, BUSINESS AI
INTEGRATION EXPERT, DATA GOVERNANCE
EXPERT, CONSULTANTS, AI RESEARCHER,
PhDs, HIGHLY EXPERIENCED EXPERT
ENGINEERS
DATA ANALYST, DATABASE DEVELOPER,
NOSQL DEVELOPERS, DATA ADMIN, BIG
DATA DEVELOPERS, DATA VISUALIZATION,
DATA HACKERS, DATA INSIGHTS ENGINEER,
NEWBIE ML ENGINEER, STATISTICIAN
DATA ENGINEER, CLOUD BIG DATA ENGINEER,
DISTRIBUTED SYSTEMS ENGINEER, DEVOPS,
BIG DATA ADMINISTRATOR
MACHINE LEARNING ENGINEER, DATA
SCIENTIST, ANALYTICS MANAGER, APPLIED AI
ENGINEER, NLP ENGINEER, OPTIMIZATION
EXPERT, STATISTICIAN, QUANTITATIVE
MODELLING EXPERT
Descriptive Analytics
[What happened?]
Predictive Analytics
[What can happen?]
Prescriptive Analytics
[What should we do when
it happens?]
Researchers, Deep Learning, AI Business integration,
Automated Insight Discovery
Controlled Repeatable Experiments, Fast Insights
Data Driven AI Organization
AI driven Actions, Cutting Edge Innovation
Machine Learning
scikit learn, tensorflow, h2o
Operation Dashboard, Real time
Analytics
Insights Actions
Big Data (Management of 4 Vs)
Data Architecture and Management
Flume, Hadoop, DW, S3
Ad-hoc Analysis](https://image.slidesharecdn.com/hiringfordataroles-181029070233/85/Hiring-for-data-roles-Adwait-Bhave-ML-Engineer-and-Data-Scientist-at-Druva-10-320.jpg)
Hiring for data roles - Adwait Bhave (ML Engineer and Data Scientist at Druva
- 1. Hiring for Data What to look for when hiring for data roles
- 2. The Problem Data Roles Designations Organizational needs
- 3. We have to understand… Data Scientist is a useless job title. It is a ‘catch-all’ term for many roles related to data. Organizations are in different stages of building up data driven culture and hence require different skillsets to move up the data value chain.
- 4. Data Science Process Define problem and how to validate solution Collect raw data. Clean data. Explore and analyse Build ideas and model Validate model. Build and validate solution. Create Insights. Visualize and explain. Operationalize. Inspire Decision.
- 5. Organizations data maturity Ad-hoc data collection and analysis. Controllable data collection. Controllable and repeatable experiments. Ability to make predictions through models. Ability to compare models. Automated insights. Faster data driven decisions.
- 6. Designations & skills https://www.kaggle.com/surveys/2017 Python 58% SQL 54% R 44% Algorithms 38% Java 32% Big Data 28% optimization 23% C++ 21% Distributed 20% visualization 20% Business Intelligence 18% Unstructured 16% Packages 15%
- 9. Conclusion Understand what phase the organization is in. What problem it is trying to solve. 1 Create the right role description. 2 Look for candidates with combination of skills. Prefer Pi profiles. 3 Dig deeper into candidates skills beyond keywords. 4
- 10. Org State and role mapping Decision workflows, Business Process Integrations Java, Python, Spark, Apache Beam, GCP Dataflow Data Pipeline / DW R, Python Visualization, D3.js Hive, BigQuery Data Analysis Collect Store Explore Operationalize Automate Predict/Understand Optimize CHIED DATA OFFICER, DATA RISK ANALYST, EXPERT ANALYTICS, BUSINESS AI INTEGRATION EXPERT, DATA GOVERNANCE EXPERT, CONSULTANTS, AI RESEARCHER, PhDs, HIGHLY EXPERIENCED EXPERT ENGINEERS DATA ANALYST, DATABASE DEVELOPER, NOSQL DEVELOPERS, DATA ADMIN, BIG DATA DEVELOPERS, DATA VISUALIZATION, DATA HACKERS, DATA INSIGHTS ENGINEER, NEWBIE ML ENGINEER, STATISTICIAN DATA ENGINEER, CLOUD BIG DATA ENGINEER, DISTRIBUTED SYSTEMS ENGINEER, DEVOPS, BIG DATA ADMINISTRATOR MACHINE LEARNING ENGINEER, DATA SCIENTIST, ANALYTICS MANAGER, APPLIED AI ENGINEER, NLP ENGINEER, OPTIMIZATION EXPERT, STATISTICIAN, QUANTITATIVE MODELLING EXPERT Descriptive Analytics [What happened?] Predictive Analytics [What can happen?] Prescriptive Analytics [What should we do when it happens?] Researchers, Deep Learning, AI Business integration, Automated Insight Discovery Controlled Repeatable Experiments, Fast Insights Data Driven AI Organization AI driven Actions, Cutting Edge Innovation Machine Learning scikit learn, tensorflow, h2o Operation Dashboard, Real time Analytics Insights Actions Big Data (Management of 4 Vs) Data Architecture and Management Flume, Hadoop, DW, S3 Ad-hoc Analysis