Job description

**Overview** Data Science Team works in developing Machine Learning (ML) and Artificial Intelligence (AI) projects. Specific scope of this role is to develop ML solution in support of ML/AI projects using big analytics toolsets in a CI/CD environment. Analytics toolsets may include DS tools/Spark/Databricks, and other technologies offered by Microsoft Azure or open\-source toolsets. This role will also help automate the end\-to\-end cycle with Azure Pipelines. You will be part of a collaborative interdisciplinary team around data, where you will be responsible of our continuous delivery of statistical/ML models. You will work closely with process owners, product owners and final business users. This will provide you the correct visibility and understanding of criticality of your developments. **Responsibilities** * Delivery of key Advanced Analytics/Data Science projects within time and budget, particularly around DevOps/MLOps and Machine Learning models in scope * Active contributor to code \& development in projects and services * Partner with data engineers to ensure data access for discovery and proper data is prepared for model consumption. * Partner with ML engineers working on industrialization. * Communicate with business stakeholders in the process of service design, training and knowledge transfer. * Support large\-scale experimentation and build data\-driven models. * Refine requirements into modelling problems. * Influence product teams through data\-based recommendations. * Research in state\-of\-the\-art methodologies. * Create documentation for learnings and knowledge transfer. * Create reusable packages or libraries. * Ensure on time and on budget delivery which satisfies project requirements, while adhering to enterprise architecture standards * Leverage big data technologies to help process data and build scaled data pipelines (batch to real time) * Implement end\-to\-end ML lifecycle with Azure Databricks and Azure Pipelines * Automate ML models deployments **Qualifications** * BE/B.Tech in Computer Science, Maths, technical fields. * Overall 2\-4 years of experience working as a Data Scientist. * 2\+ years’ experience building solutions in the commercial or in the supply chain space. * 2\+ years working in a team to deliver production level analytic solutions. Fluent in git (version control). Understanding of Jenkins, Docker are a plus. * Fluent in SQL syntaxis. * 2\+ years’ experience in Statistical/ML techniques to solve supervised (regression, classification) and unsupervised problems. * 2\+ years’ experience in developing business problem related statistical/ML modeling with industry tools with primary focus on Python or Pyspark development. * Data Science – Hands on experience and strong knowledge of building machine learning models – supervised and unsupervised models. Knowledge of Time series/Demand Forecast models is a plus * Programming Skills – Hands\-on experience in statistical programming languages like Python, Pyspark and database query languages like SQL * Statistics – Good applied statistical skills, including knowledge of statistical tests, distributions, regression, maximum likelihood estimators * Cloud (Azure) – Experience in Databricks and ADF is desirable * Familiarity with Spark, Hive, Pig is an added advantage * Business storytelling and communicating data insights in business consumable format. Fluent in one Visualization tool. * Strong communications and organizational skills with the ability to deal with ambiguity while juggling multiple priorities * Experience with Agile methodology for team work and analytics ‘product’ creation. * Experience in Reinforcement Learning is a plus. * Experience in Simulation and Optimization problems in any space is a plus. * Experience with Bayesian methods is a plus. * Experience with Causal inference is a plus. * Experience with NLP is a plus. * Experience with Responsible AI is a plus. * Experience with distributed machine learning is a plus * Experience in DevOps, hands\-on experience with one or more cloud service providers AWS, GCP, Azure(preferred) * Model deployment experience is a plus * Experience with version control systems like GitHub and CI/CD tools * Experience in Exploratory data Analysis * Knowledge of ML Ops / DevOps and deploying ML models is preferred * Experience using MLFlow, Kubeflow etc. will be preferred * Experience executing and contributing to ML OPS automation infrastructure is good to have * Exceptional analytical and problem\-solving skills * Stakeholder engagement\-BU, Vendors. * Experience building statistical models in the Retail or Supply chain space is a plus