1. Bridge the Gap Between Statistician and Data Analysis Professionals
Ming Amazon

2. Education Career Title Tasks and Requirements
Business insight, dashboard, weekly report, ad-hoc analysis: intermediate SQL, simple statistical models, few machine learning, expert in dashboard
A good balance of business analyst and data engineer: strong SQL and dashboard, simple to intermediate statistical model, maybe some machine learning models
Build data pipeline in the production environment, strong SQL, strong knowledge in production system and database, no modeling requirement
Similar to BIE, but with stronger modeling in statistics and machine learning, usually leverage available methods to solve business problems
Very strong modeling background in statistical and machine learning, not much requirement in production level programming
More specialized modeling skills (such as NLP, image recognition) and/or with production level programming proficiency
Business
Analyst
Intelligence
Engineer
Data
Scientist
Research
Applied
BS in
Statistics
MS in
PhD in
With the difference between statistician and data scientist, what types of data science career tracks maybe available? There are quite a few different career paths for statistician with different background. For large tech companies, the career tracks become more specific recently, and I will call all of them broadly as data science professions. There are three dimensions of expertise: (1) business knowledge, (2) modeling knowledge with R/python, (3) production system knowledge (i.e. Java and SQL).
Each career path has its own promotion cycle.

3. Education Career Title Skill Gaps
Business
Analyst
Intelligence
Engineer
Data
Scientist
Research
Applied
SQL, Dashboard (such as Tableau)
Business problem understanding
BS in
Statistics
MS in
PhD in
SQL, Dashboard, Production environment
SQL, Production environment, Big data plat form, Database architecture
SQL, Big data platform, Python, Dashboard
SQL, Big data platform, Python, Deep learning
Programming, SQL, Big data platform, Python, Deep learning

4. Generalist Specialist Business problem formulation skills
Data preprocessing skills
Statistical and machine learning methods
Deep learning applications knowledge
Model building process experience
Generalist
Natural language understanding
Image and video analysis
Voice recognition
Language translation
AI chat-bot
Specialist
Recent trends of Cloud-Based Solution lower the barrier for statistician drastically in this area
If you are good at all three areas, you become a "full-stack" data scientist! The best of the best!
Big data infrastructure (Spark)
Database and data retrieval (SQL)
Production environment
Programming skill (Python)
Unstructured data (image, voice, text…)
Leadership
Business understanding
Communication skills
Collaboration &Teamwork
Strategic planning & execution

5. Relatively focus on modeling (i.e. science)
Bring data to model
Usually isolated from production system
Data is relatively small in size and clean in text file formats
Usually structured data
Statis-tician
Mainly focus on business problem & result (i.e. engineering)
Bring models to data
Usually embedded in production system
Need to work with messy and large amount data in various formats
Both structured & unstructured data
Data Scientist
With the analogy of science and engineering, we can see some of the major differences between statistician and data scientist. First, statistician are relatively focus on modeling, while data scientist are mainly focus on business problem and results. (2) Statistician usually bring data to model (i.e. there is a good model, let us find some data and application); while data scientist usually bring models to data (i.e. we have a business problem, let’s try a few models to see which one is going to help). (3) Statistician usually is isolated from the production system (i.e. the entire software structure) and focus on the structured data given in text format; while data scientist has to be part of production system to leverage the data stored in various format including both structured and un-structured data. (4) Statistician tends to work with small data (i.e. can be fit into laptop’s memory), while data scientist has to deal with large amount of data far beyond a laptop’s hard disk.

6. Business problem and value Resources, milestone and timeline
Data Science Project Cycle
Data
Information
Knowledge
Insight
Decision & Action
Business problem and value
Resources, milestone and timeline

7. Project Cycle Business problem definition and understanding
Quantifying business value and define key metrics
Computation resources assessment
Key milestones and timeline
Data security, privacy and legal review
Data science formulation
Data quality and availability
Data preprocessing
Feature engineering
Model exploring and development
Model training, validation, testing
Model selection
A/B testing in production system
Model deployment in production environment
Exception management
Performance monitoring
Model tuning and re-training
Model update and add-on
Model failure and retirement
Project Cycle
As you can see, the data science project cycle is complicated. The end to end cycle contains many steps, modeling is just a small part of the entire cycle. For people in any one of the data science professionals, certain level of knowledge or experience to the end to end cycle will greatly help their career development. Of course, for a specific career path, it has its own areas of expertise to be mastered.

8. Agile-Style Project Management
Cross Team
Collaboration
Business teams
Operation team
Business analyst team
Insight and reporting team
Technology team
Database and data warehouse team
Data engineering team
Infrastructure team
Core machine learning team
Software development team
Visualization dashboard team
Production implementation
Project management team
Program management team
Product management team
Senior leadership team
Leaders across organizations
Agile-Style Project Management
During the end to end data science project cycle, a data science professional need to interact with different teams. First to define the data science project, business teams get involved, such as operation team, and its own analyst team. To interact and establish pipeline with the archived historical data and available real time online data, database, data warehouse and data engineering team. To ensure scalability, the infrastructure team and core machine learning team are needed. Finally to implement the model in production, software development and visualization dashboard team are needed. For a specific project, there is a project manager and one project maybe part of a larger program. And once it becomes a product, it has to be managed by a the product management team. And during the project, occasionally, there will be interaction with senior leadership and leaders across organizations. Data scientists are definitely work in a collaboration environments.

9. Speaking the Same Language
Communication:
Speaking the Same Language
Interact with multiple teams across the entire project cycle
Easy to understand language that everyone understand
Be clean on deliverables, timeline and resource allocation
Technical modeling part requires communication skills too
Statistician, Operation Researcher, Economist, Computer Scientist, Market Researcher, …
Need to be familiar with different terminology, for example:
Label = Target = Outcome = Class = Response = Dependent Variables
Features = Attribute = Independent Variables = Predictors = Covariates
Dimensionality = number of features
Weights = Parameters
Learning = Fitting
Generalization = Applying to population or test data
Sensitivity = recall = hit rate = true positive rate

10. Summary

11. Pretty broad career choices for data analyst professionals
Gaps between these potential career paths and statistician are:
SQL
Dashboard (such as Tableau)
Big data platform
Production environment
Python
Deep learning
Programming (i.e. writing production level code using Java)
Project cycles are complicated
Communication skills are important to be successful

12. Thank you!