Job Description

Description

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