Assistant Manager (Data Science)

LatentView; Gurugram District, Chennai District, Bengaluru; On-site; INR 3,000,000 - 6,000,000; Full time

Job summary

LatentView is seeking a senior ML leader to drive end-to-end development and deployment of machine learning solutions in a client-facing role in India. You will design survival/hazard models, engineer complex features, and lead MLOps pipelines with a focus on revenue optimization and asset returns.

You will mentor a small data science team, translate business rules into modeling roadmaps, and collaborate in an agile environment to deliver scalable data-driven strategies for clients.

Qualifications

• Strong background in end-to-end ML project delivery with leadership experience.
• Experience translating business rules into modeling roadmaps and client communication.
• Ability to mentor a small data science team in agile delivery.

Responsibilities

• End-to-End Modeling: build and train survival/hazard models for pricing and actions.
• Data & Feature Engineering: evaluate data readiness and create advanced features.
• MLOps & Deployment: test in shadow mode and collaborate on scalable pipelines.
• Leadership & Strategy: be the technical lead for clients and guide the team.

Skills

Python

SQL

PySpark

Survival modeling

Hazard modeling

MLOps

Snowflake

dbt

Tableau

Team leadership

Tools

Snowflake

dbt

Tableau

Job description

Job Description:

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.

Responsibilities:

• End-to-End Modeling: Build and train survival/hazard-based models to estimate sale probabilities and recommend item-level pricing and disposition actions.
• Data & Feature Engineering: Evaluate data readiness, engineer complex features (demand signals, seasonality, costs), and design workarounds for data gaps.
• MLOps & Deployment: Test models in shadow mode, measure real-world performance against benchmarks, and collaborate with Data Engineering to build scalable MLOps pipelines (feature stores, CI/CD).
• Leadership & Strategy: Act as the primary technical point of contact for clients. Translate complex business rules into a modeling roadmap and mentor a small team of data scientists through agile delivery.

Skills:

• Python, SQL, PySpark, Machine Learning (Survival/Hazard modeling), MLOps, Snowflake, dbt, Tableau, and Team Leadership.
• Pricing/Inventory domain expertise, constrained optimization, and cloud-native ML tooling.