Data Science Assoc Analyst

Role Overview: You will be part of the Data Science Team working on developing Machine Learning (ML) and Artificial Intelligence (AI) projects. Your specific role will involve developing ML solutions to support ML/AI projects using big analytics toolsets in a CI/CD environment. You will collaborate with a team of interdisciplinary professionals focused on data, ensuring the continuous delivery of statistical/ML models. Additionally, you will work closely with process owners, product owners, and business users to drive successful project outcomes. Key Responsibilities: - Deliver key Advanced Analytics/Data Science projects within specified timelines and budget, particularly focusing on DevOps/MLOps and Machine Learning models. - Actively contribute to code development in projects and services. - Partner with data engineers to ensure proper data access and preparation for model consumption. - Collaborate with ML engineers to support industrialization efforts. - Communicate with business stakeholders throughout the service design, training, and knowledge transfer processes. - Support large-scale experimentation and model building, refining requirements into modeling problems. - Influence product teams with data-based recommendations and stay updated on state-of-the-art methodologies. - Conduct research on statistical/ML techniques and create documentation for knowledge transfer. - Develop reusable packages or libraries and ensure timely and budget-compliant project delivery while adhering to enterprise architecture standards. - Leverage big data technologies to process data and build scaled data pipelines, implementing end-to-end ML lifecycle with Azure Databricks and Azure Pipelines. - Automate ML model deployments. Qualifications: - BE/B.Tech in Computer Science, Mathematics, or related technical fields. - 2-4 years of experience as a Data Scientist, with 2 years specifically in building solutions in commercial or supply chain domains. - Proficiency in version control tools like git, with understanding of Jenkins and Docker as a plus. - Fluent in SQL syntax. - Experience in Statistical/ML techniques for solving supervised and unsupervised problems, with a focus on Python or Pyspark development. - Hands-on experience in building machine learning models, including supervised and unsupervised models. Knowledge of Time series/Demand Forecast models is advantageous. - Strong programming skills in Python, Pyspark, and SQL. - Good applied statistical skills, familiarity with statistical tests, distributions, and regression. - Experience with cloud technologies, particularly Azure Databricks and ADF, is desirable. - Business storytelling and data insights communication skills. - Strong organizational skills, ability to manage ambiguity and multiple priorities. - Experience with Agile methodology, Reinforcement Learning, Simulation, Optimization, Bayesian methods, Causal inference, NLP, Responsible AI, distributed machine learning, and DevOps. - Familiarity with cloud service providers, version control systems, CI/CD tools, ML Ops, ML deployment, MLFlow, Kubeflow, and Exploratory data Analysis. - Exceptional analytical and problem-solving abilities and stakeholder engagement experience. (Note: The additional details of the company were not included in the provided job description.) Role Overview: You will be part of the Data Science Team working on developing Machine Learning (ML) and Artificial Intelligence (AI) projects. Your specific role will involve developing ML solutions to support ML/AI projects using big analytics toolsets in a CI/CD environment. You will collaborate with a team of interdisciplinary professionals focused on data, ensuring the continuous delivery of statistical/ML models. Additionally, you will work closely with process owners, product owners, and business users to drive successful project outcomes. Key Responsibilities: - Deliver key Advanced Analytics/Data Science projects within specified timelines and budget, particularly focusing on DevOps/MLOps and Machine Learning models. - Actively contribute to code development in projects and services. - Partner with data engineers to ensure proper data access and preparation for model consumption. - Collaborate with ML engineers to support industrialization efforts. - Communicate with business stakeholders throughout the service design, training, and knowledge transfer processes. - Support large-scale experimentation and model building, refining requirements into modeling problems. - Influence product teams with data-based recommendations and stay updated on state-of-the-art methodologies. - Conduct research on statistical/ML techniques and create documentation for knowledge transfer. - Develop reusable packages or libraries and ensure timely and budget-compliant project delivery while adhering to enterprise architecture standards. - Leverage big data technologies to process data and build scaled data pipelines, implementing end-to-end ML lifec