Roles and Responsibilities
- Own and execute end-to-end delivery of one or more analytics projects.
- Understand requirements from Product Managers/Business Users.
- Translating muddy and fuzzy business needs into clear analytical problems through structured problem-solving.
- Extract data from multiple sources, develop predictive models using appropriate variables and ML/deep learning techniques, and operationalize the models on a BI platform for end-user consumption.
- Validate the models developed against statistical robustness and business sense.
- Perform ad-hoc deep dive analysis proactively / based on stated needs − Generate insights based on the data patterns and recommend actions to be taken by the business.
- Communicate the results to technical as well as business stakeholders across different hierarchies.
- Create technical documentation and provide post-production support for a time-bound period.
Skills and Qualifications
- BE/ B. Tech / ME/ M. Tech • MBA (Analytics / IT / SCM / Operations / General Management) • M.Sc. (Statistics/ Mathematics/ Economics/ Ops Research/ Other quantitative disciplines.
- 5+ years of comprehensive data science project life cycles from use case framing, data collection, data exploration, and model building to deployment.
- Good statistics knowledge and deep understanding of ML algorithms and their usage (Time series, Regression, Classification, Clustering, Anomaly Detection, NLP, etc.).
- Deep expertise in analytical tools: − At least one statistical computing language (R, Python, Tensorflow, Keras, etc.) At least one query language (T-SQL, PLSQL, Hive, Spark, Impala, Cassandra, MongoDB, etc.).
- Knowledge of big data is essential − At least one of the reporting/visualization tools (Tableau, QlikView, SSRS, Power BI, D3, Angular JS, etc.).
- Proven record of successfully building and operationalizing a variety of machine-learning solutions.