Lead Data Scientist - BFSI

Lead Data Scientist

- Analyze large-scale structured and unstructured data; develop deep-dive analyses and machine learning models in retail, marketing, merchandising, and other areas of the business.

- Utilize data mining and statistical techniques to derive business value from store, product, operations, financial, and customer transactional data.

- Apply multiple algorithms or architectures and recommend the best model with in-depth description to evangelize data-driven business decisions.

- Utilize cloud setup to extract processed data for statistical modelling and big data analysis, and visualization tools to represent large sets of time series/cross-sectional data.

- Follow industry standards in coding solutions and follow programming life cycle to ensure standard practices across the project.

- Structure hypothesis, build thoughtful analyses, develop underlying data models, and bring clarity to previously undefined problems.

- Work collaboratively across multiple sets of stakeholders - Business functions, Data Engineers, Data Visualization experts to deliver on project deliverables.

- Articulate complex data science models to business teams and present the insights in easily understandable and innovative formats.

- Proactively recommend multiple approaches along with their pros & cons to the business stakeholders and support making the right decisions.

Qualifications and experience:

- Bachelor's degree required, preferably with a quantitative focus (Statistics, Business Analytics, Data Science, Math, Economics, etc.)

- Master's degree preferred (MBA/MS Computer Science/MTech Computer Science, etc.)

- 8- 12 years of relevant industry experience in a data science/ advanced analytics role.

- Knowledge of Functional Analytics (Supply chain analytics, Marketing Analytics, Customer Analytics)

- Knowledge and ability to conduct statistical modelling using Analytical tools ( Python, KNIME, etc.) and use big data technologies

- Knowledge of business intelligence & reporting (Power BI, Tableau, Alteryx, etc.)

- Knowledge of Enterprise reporting systems, relational (MySQL, Microsoft SQL Server etc.), non-relational database management systems and Data Engineering tools.

- Knowledge and ability to use Big data technologies (Hadoop, Spark, Kafka, Presto etc) and Cloud computing services in Azure/AWS/GCP for data engineering, ML Ops.

- Ability to delivery, strong disposition towards business and strong interpersonal communication.

- Individual must be organized, dependable, able to multi-task and manage priorities, display initiative, and must have the ability to lead teams in a demanding, fast-paced environment.