AddressLead 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.
