Senior Analyst - Data Science

Darwinbox Digital Solutions Pvt. Ltd.; Telangana; Hybrid; INR 1,200,000 - 2,200,000; Full time

Job summary

LatentView Analytics is seeking experienced Data Scientists to design, build, and operationalize statistical and machine learning solutions across client engagements. You will handle large-scale datasets, uncover patterns, and translate findings into decision-making insights.

The role spans applied ML to GenAI platform engineering, aligned with your experience. Responsibilities include model development, data engineering, deployment, governance, and clear communication to technical and

Qualifications

• 4–10 years of relevant experience in Data Science, ML, or a closely related analytics discipline.
• Strong programming skills in Python (or R) for data manipulation, model development, and evaluation.
• Proficiency in SQL for querying and processing data, including at scale (e.g. Spark SQL, Hive)
• Hands-on experience with core statistical and machine learning techniques — regression, classification, clustering, or dimensionality reduction — using libraries such as scikit-learn or statsmodels
• Experience working with large-scale or distributed data platforms such as Spark, Databricks, or equivalent cloud environments
• Strong stakeholder management and communication skills — proven ability to translate technical findings into business insights

Responsibilities

• Model development: Design, build, and evaluate statistical and machine learning models against real business problems, from exploratory analysis through to production-quality solutions
• Data engineering for analysis: Acquire, clean, and process data from large-scale or distributed sources, including feature engineering to strengthen model performance
• Deployment & monitoring: Operationalize models into production pipelines, and monitor performance over time to catch drift, decay, or data-quality issues
• Stakeholder collaboration: Work closely with business, product, engineering, risk, or compliance teams — as relevant to the project — to shape and deliver analytical solutions
• Communication: Present findings, models, and recommendations clearly to both technical and non-technical audiences
• Governance & reliability: Follow responsible AI, data governance, and security practices appropriate to the engagement

Skills

Python

SQL

Statistical methods

Machine Learning

Data Engineering

Education

Bachelor's or Master's in a quantitative field

Tools

Spark

Databricks

Spark SQL

Hive

Docker

Kubernetes

Cloud platforms (GCP/AWS)

Job description

Founded in 2006, LatentView Analytics began with a shared passion for the world of data. Today, over 20 years on, we've grown into a close-knit community of people united by that same drive — solving real business challenges with data and AI. We work with industry leaders worldwide, specializing in end-to-end analytics that goes beyond the buzzwords to deliver genuine business impact. Our focus has stayed consistent since day one: helping clients derive meaningful insights and drive growth through a thoughtful, sustainable approach to data analytics and AI.

Role Overview

We are hiring Data Scientists to design, build, and operationalize statistical and machine learning solutions across our client engagements. You will work with large-scale and often distributed datasets to uncover patterns, build predictive models, and translate findings into decisions that matter to the business. The specific focus of your role — from applied ML and marketing analytics to platform engineering for GenAI — will be matched to your experience and specialization.

Key Responsibilities

Model development: Design, build, and evaluate statistical and machine learning models against real business problems, from exploratory analysis through to production-quality solutions

Data engineering for analysis: Acquire, clean, and process data from large-scale or distributed sources, including feature engineering to strengthen model performance

Deployment & monitoring: Operationalize models into production pipelines, and monitor performance over time to catch drift, decay, or data-quality issues

Stakeholder collaboration: Work closely with business, product, engineering, risk, or compliance teams — as relevant to the project — to shape and deliver analytical solutions

Communication: Present findings, models, and recommendations clearly to both technical and non-technical audiences

Governance & reliability: Follow responsible AI, data governance, and security practices appropriate to the engagement

Skills & Experience

4–10 years of relevant experience in Data Science, Machine Learning, or a closely related analytics discipline

Strong programming skills in Python (or R) for data manipulation, model development, and evaluation

Proficiency in SQL for querying and processing data, including at scale (e.g. Spark SQL, Hive)

Hands-on experience with core statistical and machine learning techniques — regression, classification, clustering, or dimensionality reduction — using libraries such as scikit-learn or statsmodels

Experience working with large-scale or distributed data platforms such as Spark, Databricks, or equivalent cloud environments

Strong stakeholder management and communication skills — proven ability to translate technical findings into business insights

Good to Have(any one combination)

Hands-on GenAI/LLM experience in production, with platforms such as AWS Bedrock, Azure OpenAI, OpenAI, or Anthropic

REST APIs, RDBMS, and enterprise backend/platform engineering experience

AI governance, security, guardrails, observability, and cost/token optimization

Experience leading or mentoring platform engineering teams

Marketing Analytics & Bayesian Modeling

Bayesian inference and MCMC, with hands-on experience in PyMC or Stan

Marketing Mix Modeling (MMM), adstock and saturation/Hill transformations

ROAS, media attribution, and channel decomposition

Time-series econometrics and constrained optimization for budget allocation

Experience deploying models with tools such as Docker, Kubernetes, or cloud ML platforms (e.g. GCP Vertex AI, AWS SageMaker)

Building feature pipelines and workflows that support scalable model training and scoring

Anomaly detection techniques and model explainability methods (e.g. SHAP, LIME)

Monitoring frameworks for model performance, data drift, and retraining triggers

Risk, Fraud & Compliance Analytics

Experience building fraud or financial-crime detection models, including exposure to platforms such as Feedzai

Domain exposure to FinTech, Payments, or Banking

Working within regulatory and compliance-driven environments

Distributed Data & Domain Foundations

PySpark or SparkR, with experience on Databricks, AWS EMR, or equivalent platforms

Working knowledge of a business domain such as Banking and Financial Services, Retail, Healthcare, or Telco

Familiarity with data governance concepts and structured analytical workflows

Qualifications

Bachelor's or Master's degree in a quantitative field (Computer Science, Statistics, Applied Mathematics, Engineering, or related) or equivalent practical experience

Strong analytical, problem-solving, and communication skills

Exposure to version control (Git) and collaborative development practices