Role & Responsibilities -

- Lead and Own the Thought Process on one or more of our core Data Science problems e.g. Product Clustering, Intertemporal Optimization, etc.

- Actively participate and challenge assumptions in translating ambiguous business problems into one or more ML/optimization problems

- 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

- Define and own metrics on solution quality, data quality and stability of ML pipelines

- 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.

- Minimum of 3+ years experience in a data science role in a technology company

- Solid mathematical background (especially in linear algebra, probability theory, optimization theory, decision theory, operations research)

- 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

- A solid foundation in data structures, algorithms, and programming language theory

- 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 focus areas of the company Data Science team:

(a) Product Clustering, (b) Demand Forecasting, (c) Intertemporal Optimization, (d) Reinforcement Learning, (e) Transfer Learning

- 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)

- Experience using Cloud-based ML platforms (e.g., AWS Sagemaker, Azure ML), Cloud-based data storage, and deploying ML models in product environment in collaboration with engineering teams

- 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

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