We are looking to hire for our data science team. The data science team at WyngCommerce is on a mission to challenge the norms and re-
imagine how retail business should be run across the world. As a Junior 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 ML-
based solutions and fine tune them with more data. This is a high impact role with goals that directly impact our business.
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.
Communicate results to stakeholders and present data / insights to participate in and drive decision making.
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 : Anomaly Detection, Time Series Analysis, Product Clustering, Demand Forecasting, 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.
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 (Plus Point).
Research experience and publications in top ML / Data science conferences (Nice to have).