Data Scientist - IV - AI/ML Ops Engineer (Pune)

Role & responsibilities Promotes learning in others by proactively providing and/or developing information, resources, advice, and expertise with coworkers and members; builds relationships with cross-functional/external stakeholders and customers. Listens to, seeks, and addresses performance feedback; proactively provides actionable feedback to others and to managers. Pursues self-development; creates and executes plans to capitalize on strengths and develop weaknesses; leads by influencing others through technical explanations and examples and provides options and recommendations. Adopts current responsibilities; adapts to and learns from change, challenges, and feedback; demonstrates flexibility in approaches to work; champions change and helps others adapt to new tasks and processes. Facilitates team collaboration to support a business outcome. - Completes work assignments autonomously and supports business-specific projects by applying expertise in subject area and business knowledge to generate creative solutions; encourages team members to adapt to and follow all procedures and policies. Collaborates cross-functionally and/or externally to achieve effective business decisions; provides recommendations and solves complex problems; escalates high-priority issues or risks, as appropriate; monitors progress and results. Supports the development of work plans to meet business priorities and deadlines; identifies resources to accomplish priorities and deadlines. Identifies, speaks up, and capitalizes on improvement opportunities across teams; uses influence to guide others and engages stakeholders to achieve appropriate solutions. - Develops detailed problem statements outlining hypotheses and their effect on target clients/customers by defining scope, objectives, outcome statements and metrics. - Designs and develops data pipelines and automation for data acquisition and ingestion of raw data from multiple data sources and data formats by transforming, cleansing, and storing data for consumption by downstream processes; writing and optimizing diverse SQL queries; and demonstrating advanced knowledge of database fundamentals. - Analyzes and investigates complex data sets and summarizes key characteristics by employing data visualization methods; and determining how best to manipulate data sources to discover patterns, spot anomalies, test hypotheses, and/or check assumptions. - Selects, manipulates, and transforms data into features used in machine learning algorithms by leveraging techniques to conduct dimensionality reduction, feature importance, and feature selection. - Trains statistical models by using algorithms and data mining techniques; testing models with various algorithms to assess the input dataset and related features; and applying techniques to prevent overfitting such as cross-validation. - Deploys and maintains reliable and efficient models through production. - Verifies model performance by demonstrating expertise in the practice of a variety of model validation techniques to assess and discriminate the goodness of model fit; and leveraging feedback and output to manage and strengthen model performance. - Collaborates with internal and external stakeholders across domains to develop and deliver statistical driven outcomes by delivering insights and values from heterogeneous data to investigate complex problems for multiple use cases; driving informed decision-making; and presenting findings to both technical and non-technical audiences. Preferred candidate profile MLOps and AI platform exposure (model deployment/monitoring, tools like SageMaker, Vertex AI, MLflow). SRE practices (SLIs/SLOs, error budgets, incident management, runbooks, postmortems). IT operations domain and soft skills (ITIL/ITSM, ticketing tools, clear communication, structured problem solving). .