Description :

Senior Data Scientist

Location : Bangalore (Client-facing role; travel across India / Southeast Asia possible)

Experience : 512 years

About Greyamp :

Greyamp Consulting is a boutique transformation consulting firm that works with large enterprises across Southeast Asia and India to turn strategy into execution.

Over the past decade, our work has focused on operating model transformation and large-scale program activation. Today, we are expanding our focus toward data and AI-led transformation, helping organizations move from analytics experimentation to production-grade data and AI systems that drive real business decisions.

Our teams work closely with enterprise clients and global partners to design, build, and operationalize AI-driven solutions across industries such as manufacturing, mining, and healthcare.

Joining Greyamp at this stage offers a unique opportunity to work on high-impact transformation programs, build production-grade AI solutions, and collaborate with consulting teams and client stakeholders to solve complex business problems using data and AI.

Role Overview :

We are looking for a Senior Data Scientist who can translate business problems into deployable analytics, machine learning, and AI solutions.

This is not a purely research-focused role.

Our data scientists work closely with consulting teams, data engineers, and client stakeholders to design models and analytical systems that improve real business decisions and operational workflows.

You will be responsible for framing analytical problems, developing predictive and machine learning models, and ensuring that these solutions can be deployed and adopted in production environments.

Role : Senior Data Scientist

Location : Bangalore (Client-facing role; travel across India / Southeast Asia possible) :

Experience : 512 years

What You Will Work On :

You will contribute to enterprise data and AI programs such as :

- Designing predictive models that support operational decision making

- Developing fraud detection, risk scoring, and forecasting solutions

- Building recommendation engines and decision-support models

- Creating AI and LLM-enabled copilots for enterprise workflows

- Deploying analytics models that integrate directly into business processes

Your work will move beyond model experimentation you will help ensure that models deliver measurable business impact.

Key Responsibilities :

Problem Framing & Analytics Design :

- Work with consulting teams and client stakeholders to translate business problems into analytical use cases

- Define metrics, hypotheses, and analytical approaches for solving enterprise problems

- Identify relevant data sources and design analytical frameworks

Model Development & Evaluation :

- Develop predictive models using statistical and machine learning techniques

- Perform exploratory data analysis (EDA) to identify patterns and insights

- Implement feature engineering, model selection, and performance evaluation

Production Deployment & Impact :

- Work with data engineers to productionize models and analytics workflows

- Monitor model performance and ensure reliability over time

- Ensure that model outputs integrate effectively into operational workflows

Collaboration & Delivery :

- Present insights and analytical findings to both technical and business stakeholders

- Collaborate with cross-functional teams including engineers, consultants, and client teams

- Contribute to solution design discussions for enterprise AI programs

Required Experience :

512 years of experience in data science, machine learning, or advanced analytics roles

Hands-on experience building and deploying predictive models in production environments

Strong experience working with large datasets and modern analytics platforms

Core Technical Skills :

Programming & Data Analysis :

- Strong proficiency in Python

- Strong SQL capabilities for working with large datasets

- Experience with data manipulation libraries such as Pandas or Polars

Machine Learning :

- Experience with supervised and unsupervised machine learning techniques

- Strong understanding of feature engineering, model selection, and evaluation methods

- Experience using modern ML libraries such as scikit-learn, XGBoost, LightGBM, or CatBoost

Analytics & Statistical Thinking :

- Strong foundation in statistics, hypothesis testing, and experimental design

- Ability to translate business questions into measurable analytical approaches

Data Platforms :

- Experience working with modern data platforms such as Databricks, Snowflake, or BigQuery

- Familiarity with cloud-based ML platforms such as AWS SageMaker, Vertex AI, or Azure ML

Preferred Experience :

- Experience deploying ML models into production environments

- Exposure to MLOps practices (experiment tracking, model monitoring, versioning)

- Experience working with LLMs or generative AI applications

- Familiarity with feature stores, model monitoring tools, or ML pipelines

- Experience working in consulting or client-facing analytics roles

What Makes Someone Successful in This Role :

We look for data scientists who :

- Think in terms of business impact, not just model performance

- Can convert ambiguous business problems into structured analytical solutions

- Are comfortable working in consulting-led transformation programs

- Balance statistical rigor with practical delivery

- Can communicate complex analytical insights clearly to non-technical stakeholders