Job Description

Overview: 

• Leading AI-driven Global Supply Chain Solutions Software Product Company and one of Glassdoor’s “Best Places to Work” 

• Seeking highly skilled individual with keen focus on research and prototyping and developing production grade ML solutions 

Scope: 

• Work as entry level data scientist in an applied machine learning R and D team solving very large scale supply chain problems like demand forecasting, inventory optimisation

What you’ll do: 

• Read existing conference papers and academic literature to identify the SOTA methods
• Quickly prototype new approaches to showcase to the functional stakeholders
• Write production grade code and peer review
• Publish patents and research papers in top tier international conferences
• Gain supply chain knowledge to identify and build better solutions

What we are looking for:  

• Bachelor’s degree (STEM preferred) and minimum 1+ years of experience
• Statistics and probability - Good applied statistical skills and knowledge of covariance, correlation, independence, hypothesis tests, probability distributions, Bayesian probability concepts, maximum likelihood estimators, sampling methods   
• Machine learning – In depth understanding of machine learning algorithms based on classic statistical approaches, tree based and deep learning
• Data Wrangling and visualisation – SQL, proficiency in numpy, pandas, scikit learn, matplotlib, seaborn
• Programming – python/R,  keras/tensorflow/pytorch, Linear programming solvers like cplex/gurobi/pulp. Good understanding of time space complexity
• Develop and execute automated test cases to measure and reliability of solutions
• Optimisation – knowledge of mathematical optimisation methods like Linear programming, genetic algorithms, particle swarm, reinforcement learning