Data Science Assoc Analyst

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