Data Science Manager

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 Pythons 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