Data Scientist

Eqaim Technology And Services; Delhi; Híbrido; INR 1.200.000 - 2.400.000; Jornada completa

Descripción de la vacante

Eqaim Technology And Services in Delhi, India is seeking a mid-level Data Scientist to own the full analytical and modelling lifecycle from framing an ambiguous business question to deploying a model or delivering a decision-ready insight. You will work with product, business, and engineering teams and help raise the team's AI literacy.

You will design and deploy predictive models, collaborate on data pipelines, and communicate insights to stakeholders, ensuring governance and responsible AI

Formación

• Bachelor's degree is required; Master's preferred.
• 4–12 years of Data Scientist experience owning end-to-end ML workflows.
• Strong Python and ML libraries proficiency.
• Experience with experiment tracking and governance.
• Ability to translate insights to business decisions and communicate with non-technical stakeholders.

Responsabilidades

• Own the full analytical lifecycle from framing an ambiguous business question to deploying a model or delivering a decision-ready insight.
• Design, build, and deploy predictive and ML models into production or decision-support workflows.
• Own evaluation, validation, and iterative improvement of models.
• Own A/B test design, execution, and analysis; communicate statistical significance clearly.
• Partner with Data Engineers to define data requirements and feature pipelines; build analytical datasets.

Conocimientos

Python (Pandas/NumPy)

ML libraries (XGBoost/LightGBM)

SQL (window functions)

Experiment tracking (MLflow/Weights &)

Cloud basics

TensorFlow / PyTorch

Educación

Bachelor's degree

Master's in Data Science / Statistics / ML

Herramientas

TensorFlow

PyTorch

Descripción del empleo

Data Scientist

Level: Mid-level ( 4-12 years of experience)

About the Role

We are looking for a Data Scientist to own the full analytical and modelling lifecycle, from framing an ambiguous business question to deploying a model or delivering a decision-ready insight. You will work closely with product, business, and engineering partners, and your work will directly shape product and business decisions. You will also use AI tools thoughtfully across your workflow and help raise the team's AI literacy.

Key Responsibilities

Statistical & Quantitative Analysis

• Own the full analytical lifecycle: preprocessing, exploratory data analysis, and statistical modelling.
• Deliver insights framed in business terms that drive clear decisions.

Model Development & Deployment

• Design, build, and deploy predictive and ML models (regression, classification, clustering, time-series) into production or decision-support workflows.
• Own evaluation, validation, and iterative improvement of models.

Experimentation

• Own A/B test design, execution, and analysis.
• Communicate statistical significance and uncertainty clearly to product and business stakeholders.

Data Pipeline Contribution

• Partner with Data Engineers to define data requirements and feature pipelines.
• Build or maintain analytical datasets and feature stores.

Stakeholder Collaboration

• Translate ambiguous business questions into well-defined analytical problems.
• Communicate findings to both technical and non-technical audiences.

Data Quality & Governance

• Champion data quality across modeling workflows.
• Enforce governance policies and best practices.

AI Enablement

• Integrate AI tools across the modeling lifecycle, including EDA, feature engineering, development, and communication.
• Mentor junior scientists on responsible AI use and contribute to the team's AI literacy practices.

Requirements

Education

• Bachelor's degree required.
• Master's in Data Science, Statistics, Machine Learning, or an equivalent field preferred.

Experience

• 4-12 years as a Data Scientist or in an equivalent ML role, owning model development and experimentation workflows end to end.
• A track record of insights that directly influenced product or business decisions.

Technical Skills

• Strong Python (Pandas, NumPy, Scikit-learn), with working-level experience in XGBoost, LightGBM, TensorFlow, or PyTorch.
• SQL, including window functions.
• Experience with experiment tracking tools such as MLflow, Weights & Biases, or similar.
• Basic familiarity with a cloud platform.

Statistics

• Solid grounding in hypothesis testing, regression, and experimental design.
• Understanding of causal inference concepts (e.g., difference-in-differences, regression discontinuity).
• Ability to communicate statistical uncertainty to non-technical stakeholders.

AI Literacy & Enablement

• Integrates AI tools end to end across the modeling lifecycle (EDA, feature engineering, development, communication).
• Uses advanced iterative prompting to accelerate experiment design, code generation, and reporting.
• Applies structured governance and validation to AI-generated outputs.
• Mentors junior colleagues on responsible AI use.

Success in This Role Looks Like

• Models and analyses that are trusted, reproducible, and used in real decisions.
• Experiments that are well designed, correctly interpreted, and clearly communicated.
• Strong partnerships with Data Engineering, Product, and business teams.
• A team that uses AI tools faster and more responsibly because of your contributions.