Data Scientist

LatentView; Chennai, Bengaluru; Híbrido; INR 2.500.000 - 5.000.000; Jornada completa

Descripción de la vacante

LatentView should lead the end-to-end development and deployment of Machine Learning solutions to drive strategic business value for clients. The role emphasizes robust data-driven decisioning systems that optimize revenue, asset returns, and lifecycle strategies across engagements.

Candidate will build models, engineer features, validate models, and collaborate with data engineering for scalable MLOps pipelines, CI/CD, and monitoring, while partnering with clients and mentoring a small team of

Formación

• Proficiency in Python, SQL and PySpark with hands-on ML deployment experience.
• Experience building survival/hazard models and pricing/revenue optimization is a plus.
• Familiarity with MLOps, feature stores, CI/CD and model monitoring.

Responsabilidades

• End-to-End Modeling: Build, train, and deploy ML models for pricing and disposition actions.
• Data & Feature Engineering: Assess data readiness and create meaningful features from demand signals and trends.
• Model Validation & MLOps: Test in shadow mode and integrate scalable ML pipelines with monitoring.
• Business & Client Partnership: Translate business requirements into scalable ML solutions and roadmaps.
• Leadership & Delivery: Act as technical lead for client engagements and mentor a DS team.

Conocimientos

Python

SQL

PySpark

Machine Learning

Survival Analysis

Hazard Modeling

MLOps

Snowflake

dbt

Tableau

Team Leadership

Descripción del empleo

Job Summary

Drive strategic business value by leading the end-to-end development and deployment of Machine Learning solutions. The primary focus is to build robust, data-driven decisioning systems that help clients optimize revenue, maximize asset returns, and automate complex lifecycle strategies.

Roles and Responsibilities

• End-to-End Modeling: Build, train, and deploy machine learning models, including survival and hazard-based models, to estimate sale probabilities and recommend item-level pricing and disposition actions.
• Data & Feature Engineering: Assess data readiness, perform feature engineering, and develop meaningful features using demand signals, seasonality, costs, historical trends, and other relevant business variables. Design effective approaches to address data gaps and quality issues.
• Model Validation & MLOps: Test models in shadow mode, evaluate real-world performance against established benchmarks, and collaborate with Data Engineering teams to develop scalable MLOps pipelines, including feature stores, CI/CD, model monitoring, and deployment.
• Business & Client Partnership: Work closely with clients and business stakeholders to understand complex business requirements and translate business rules into scalable machine learning solutions and modeling roadmaps.
• Leadership & Delivery: Act as a technical point of contact for client engagements, drive agile delivery, collaborate with cross-functional teams, and mentor a small team of Data Scientists.

Required Skills

• Python, SQL, PySpark, Machine Learning, Survival Analysis, Hazard Modeling, MLOps, Snowflake, dbt, Tableau, and Team Leadership.
• Strong understanding of statistical modeling, feature engineering, model validation, experimentation, and production ML deployment.
• Experience with Pricing, Inventory, Revenue Optimization, or Lifecycle Management is preferred.
• Knowledge of constrained optimization, decisioning systems, cloud-native ML tooling, model monitoring, feature stores, and CI/CD is an added advantage.