Data Scientist - IT - IIT/NIT/BITS

Data Scientist

Your Role & Responsibilities:

- Implement data-driven solutions based on advanced ML and optimization algorithms to address business problems

- Research, experiment, and innovate ML/statistical approaches in various application areas of interest and contribute to IP

- Partner with engineering teams to build scalable, efficient, automated ML-based pipelines (training /evaluation /monitoring)

- Deploy, maintain, and debug ML/decision models in a production environment

- Analyze and assess data to ensure high data quality and correctness of downstream processes

- Communicate results to stakeholders and present data/insights to participate in and drive decision making

Desired Skills & Experiences:

- Bachelors or Masters in a quantitative field from a top tier college

- 1-2 years experience in a data science/analytics role in a technology/analytics company

- Solid mathematical background (especially in linear algebra & probability theory)

- Familiarity with theoretical aspects of common ML techniques (generalized linear models, ensembles, SVMs, clustering algos, graphical models, etc.), statistical tests/metrics, experiment design, and evaluation methodologies

- Demonstrable track record of dealing with ambiguity, prioritizing needs, bias for iterative learning, and delivering results in a dynamic environment with minimal guidance

- Hands-on experience in at least one of the following: (a) Anomaly Detection, (b) Time Series Analysis, (c) Product Clustering, (d) Demand Forecasting, (e) Intertemporal Optimization

- Good programming skills (fluent in Java/Python/SQL) with experience of using common ML toolkits (e.g., sklearn, tensor flow, keras, nltk) to build models for real-world problems

- Computational thinking and familiarity with practical application requirements (e.g., latency, memory, processing time)

- Excellent written and verbal communication skills for both technical and non-technical audiences

- (Plus Point) Experience of applying ML / other techniques in the domain of supply chain - and particularly in retail - for inventory optimization, demand forecasting, assortment planning, and other such problems

- (Nice to have) Research experience and publications in top ML/Data science conferences