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Tredence : Manager - Data Science
Brief Description of position:
Job Description:
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 modelling 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.
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.
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