Tredence - Manager - Data Science

Job Summary

- Graduate degree in a quantitative field (CS, statistics, applied mathematics, machine learning, or related discipline)

- Good programming skills in Python with strong working knowledge of Python's numerical, data analysis, or AI frameworks such as NumPy, Pandas, Scikit-learn, etc.

- Experience with SQL, Excel, Tableau/ Power BI, PowerPoint

- Predictive modeling experience in Python (Time Series/ Multivariable/ Causal)

- Experience applying various machine learning techniques and understanding the key parameters that affect their performance

- Experience of building systems that capture and utilize large data sets to quantify performance via metrics or KPIs

- Excellent verbal and written communication

- Comfortable working in a dynamic, fast-paced, innovative environment with several ongoing concurrent projects.

Job Location

Bangalore/Chennai/Gurgaon/Pune

Roles & Responsibilities

- Lead a team of Data Engineers, Analysts and Data scientists to carry out following activities:

- Connect with internal / external POC to understand the business requirements

- Coordinate with right POC to gather all relevant data artifacts, anecdotes, and hypothesis

- Create project plan and sprints for milestones / deliverables

- Spin VM, create and optimize clusters for Data Science workflows

- Create data pipelines to ingest data effectively

- Assure the quality of data with proactive checks and resolve the gaps

- Carry out EDA, Feature Engineering & Define performance metrics prior to run relevant ML/DL algorithms

- Research whether similar solutions have been already developed before building ML models

- Create optimized data models to query relevant data efficiently

- Run relevant ML / DL algorithms for business goal seek

- Optimize and validate these ML / DL models to scale

- Create light applications, simulators, and scenario builders to help business consume the end outputs

- Create test cases and test the codes pre-production for possible bugs and resolve these bugs proactively

- Integrate and operationalize the models in client ecosystem

- Document project artifacts and log failures and exceptions.

- Measure, articulate impact of DS projects on business metrics and finetune the workflow based on feedbacks