Data Scientist I

Job Description

Under supervision leverage data to arrive at business solutions and creates prototypes of the solution to define analytics roadmap for clients thereby bringing in business to the organization.Outcomes:

1. Work with stakeholders and creates quick prototypes of the solution to define analytics roadmap.
2. Work with business to understand business domain and convert business problems into analytics problem
3. Create or use existing frameworks to test and validate new models
4. Explain models and put results in easy to interpret manner such that non-analytic person can understand
5. Turn data into information that can improve current workflow or processes that will help to make better business decisions. Generate business insights to help clients with decision making.
6. Adhere to best practices of coding
7. Set FAST goals and provide feedback on FAST goals of reportees

Measures of Outcomes:

1. Schedule Adherence
2. Quality of code and other deliverables
3. Number of reusable components developed
4. Number of new analytics solutions that got approved for further development

Outputs Expected:Statistical Techniques:

1. Apply statistical techniques like regression properties of distributions statistical tests etc. to analyse data.

Machine Learning Techniques:

1. Apply machine learning techniques like clustering decision tree learning artificial neural networks etc to streamline data analysis.

Creating advanced algorithms:

1. Create advanced algorithms and statistics using regression simulation scenario analysis modelling etc.

Data Visualization:

1. Visualizing/presenting data for stakeholders using: Periscope Business Objects D3 ggplot etc.

Management and Strategy:

1. Oversees the activities of analyst personnel and ensures the efficient execution of their duties.

Critical business insights:

1. Mines the business s database in search of critical business insights and communicates findings to the relevant departments.

Code:

1. Creating efficient and reusable code meant for the improvement manipulation and analysis of data.

Version Control:

1. Manages project codebase through a version control tool like git bitbucket etc.

Predictive analytics:

1. Seeks to determine likely outcomes by detecting tendencies in descriptive and diagnostic analysis

Prescriptive analytics:

1. Attempts to identify what business action to take

Create Reports:

1. Create reports depicting the trends and behaviours from the analysed data
2. Training end users on new reports and dashboards.
3. Set FAST goals and provide feedback to FAST goals of mentees

Document:

1. Create documentation for own work as well as perform peer review of documentation of others work

Manage knowledge:

1. Consume and contribute to project related documents share point libraries client universities

Status Reporting:

1. Report status of tasks assigned
2. Comply to project related reporting standards/process

Solution Architecture:

1. Create modular architecture such that it can be reused easily across projects.

Analysis:

1. Report trends for anomalies outliers trend changes and opportunities from a given data set

Data Gathering:

1. Partner with business and gather relevant data

Modelling:

1. Data models and experiments for solving business problems

Stakeholder management:

1. Explain findings to both technical and nontechnical stakeholder.

New business development:

1. Create solution architecture and quick prototypes to explain analytics roadmap to a business team.

Team Management:

1. Increase team productivity by upskilling them technically.

Skill Examples:

1. Excellent pattern recognition and predictive modelling skills
2. Extensive background in data mining and statistical analysis
3. Expertise in machine learning techniques and creating algorithms.
4. Ability to work with structured semi-structured and unstructured datasets.
5. Ability to learn and implement new Data Science algorithms in a fast turnaround time
6. Analytical Skills: Ability to work with large amounts of data: facts figures and number crunching.
7. Communication Skills: Communicate effectively with diverse people at various organization levels with the right level of detail.
8. Critical Thinking: Data analysts must look at the numbers trends and data and come to new conclusions based on the findings.
9. Strong meeting facilitation skills as well as presentation skills.
10. Attention to Detail: Making sure to be vigilant in the analysis to come to correct conclusions.
11. Mathematical Skills to estimate numerical data.
12. Work in a team environment
13. Ask proactively for help and offer help proactively
14. Break big problem into small components and define the solution architecture.
15. Explains complex models (like CNN RNN XGBoost etc.) in a manner which is easy to understand for a nontechnical stakeholder.
16. Have good understanding of known data science tools and technology like H20 Neo4j PySpark etc.

Knowledge Examples:,

Keyskills :
sqlpythonanalyticsmachine learningfast turnarounddata sciencedata miningneural networksversion controldata analysisartificial neural networks