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24/05 Nancy Chib
Consultant at Confidential

Views:3941 Applications:374 Rec. Actions:Recruiter Actions:34

Senior Data Scientist - eCommerce (3-6 yrs)

Bangalore Job Code: 1100436

- One of the leading product based ecommerce company is looking out for senior data scientist

- They are building state of the art AI software for the Global Consumer Brands & Retailers to enable best-in-class customer experiences. Our vision is to democratise machine learning algorithms for our customers and help them realise dramatic improvements in speed, cost and flexibility. Backed by a clutch of prominent angel investors & having some of the category leaders in the retail industry as clients, we are looking to hire for our data science team. 

- The data science team is on a mission to challenge the norms and re-imagine how retail business should be run across the world. As a Senior Data Scientist in the team, you will be driving and owning the thought leadership and impact on one of our core data science problems. You will work collaboratively with the founders, clients and engineering team to formulate complex problems, run Exploratory Data Analysis and test hypotheses, implement MLbased solutions and fine tune them with more data. This is a high impact role with goals that directly impact our business.

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 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 

- 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 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

Women-friendly workplace:

Maternity and Paternity Benefits

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