Lead Data Scientist

Location

Remote restrictions apply

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Salary Estimate

N/A

Seniority

Lead

Tech stacks

Machine Learning

Data

Data Science

+30

Permanent role

2 days ago

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Lead Data Scientist (m/f)

📍 India | 🕒 Full-Time | Remote

Experience: 8+ years

Role type: Individual Contributor

Function: Data Science & Machine Learning

Company Description

ebp Global is a high-performing boutique consultancy firm best known for delivering tailored, impactful solutions to our clients’ most complex problems, from conceptualisation to implementation. Our expertise covers a wide range of value chain activities from strategy, organisational design and operating models, through operations and business process optimisation, to information flows and analytics. It is through our hands-on approach, and deep knowledge that we are proud to claim some of the world’s most well-known companies, across a wide variety of industries as long-term client partners.

We are uniquely global, not just operating on a global scale but operating in a global nature, with one another and our clients too. Our team is made up of experts with operational, industry related experience; instilling a true understanding of our client’s problems with a passion to solve and improve.

See https://ebp-global.com/ for further details about our company.

Role Summary

As a Lead Data Scientist at ebp Global, you will lead the design, development, validation, and operationalization of machine learning and advanced analytics solutions that power intelligent products and business capabilities. This role combines strong hands-on expertise in model development with practical experience in MLOps, deployment, monitoring, and lifecycle management.

You will work closely with product managers, domain experts, engineers, architects, and platform teams to turn business problems into scalable, production-grade ML solutions. Beyond building models, you will guide feature engineering strategies, experimentation approaches, validation standards, and production-readiness practices to ensure models are reliable, explainable, and maintainable in real-world environments.

This role is ideal for someone who is equally comfortable developing models, operationalizing them in production, and mentoring others to raise the maturity of data science and ML engineering practices across the team.

Key Responsibilities

Lead the design, development, evaluation, and deployment of machine learning models for predictive, classification, recommendation, anomaly detection, forecasting, and optimization use cases.
Translate business and product requirements into well-defined analytical approaches, model strategies, feature sets, evaluation methods, and deployment plans.
Build robust and reusable pipelines for data preparation, feature engineering, model training, validation, hyperparameter tuning, and model packaging.
Develop and operationalize production-grade ML solutions with strong focus on reproducibility, maintainability, scalability, and measurable business impact.
Partner with data engineers and software engineers to integrate models into applications, APIs, workflows, and downstream business systems.
Design and implement MLOps practices including experiment tracking, model versioning, automated deployment, CI/CD for ML, monitoring, drift detection, retraining strategies, and rollback readiness.
Establish model performance baselines and monitor production behaviour for accuracy, drift, latency, stability, explainability, and business outcomes.
Contribute to best practices for model governance, feature lineage, documentation, testing, interpretability, and responsible AI.
Guide technical decisions on ML solution design, operationalization patterns, and production support expectations.
Work with tools and platforms such as Azure Machine Learning, Databricks, MLflow, Azure DevOps, GitHub, Docker, Kubernetes, Azure Functions, Azure Container Apps, Azure Monitor, and Application Insights (or equivalent platforms and tools).

Required Qualifications

8+ years of experience in data science, machine learning, applied AI, or advanced analytics, including strong experience delivering ML solutions in production or product environments.
Proven hands-on experience developing and deploying production-grade machine learning models - not just analytical prototypes or notebooks.
Strong expertise in supervised and unsupervised learning, including model selection, feature engineering, validation, tuning, and performance interpretation.
Strong proficiency in Python and common ML / data science libraries such as scikit-learn, pandas, NumPy, XGBoost, LightGBM, PyTorch, TensorFlow, or equivalent frameworks.
Experience building end-to-end ML pipelines across data preparation, feature engineering, model training, evaluation, deployment, and monitoring.
Practical experience with tools such as Azure Machine Learning, Databricks, MLflow, Azure DevOps, GitHub Actions, Docker, Kubernetes, Azure Functions, Azure Container Apps, or equivalent MLOps and cloud platforms.
Strong understanding of data engineering and model integration patterns, including working with SQL, batch pipelines, streaming data, APIs, and application services.
Strong understanding of ML quality dimensions such as bias, overfitting, data leakage, model drift, explainability, reproducibility, and performance stability.
Ability to translate business problems into scalable ML solutions and guide them through the full SDLC from design through deployment and continuous improvement.
Strong communication and collaboration skills, with the ability to explain technical trade-offs and model outcomes to both technical and non-technical stakeholders.
Bachelor's or Master's degree in Computer Science, Statistics, Mathematics, Engineering, or a related quantitative discipline (or equivalent practical experience).