ROLE SUMMARY
As a Lead Data Scientist, 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. In addition to 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 behavior 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
- Mentor other data scientists and ML engineers on modeling rigor, experimentation practices, and production-readiness standards
- Contribute reusable assets such as feature templates, modeling utilities, evaluation frameworks, deployment patterns, and internal accelerators
- 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
- Hands-on experience with MLOps practices and platforms, including experiment tracking, model registries, deployment automation, CI/CD for ML, model monitoring, and drift detection
- 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
- Experience working with feature stores, model registries, experiment tracking tools, and production model monitoring approaches
- Strong understanding of data engineering and model integration patterns, including working with SQL, batch pipelines, streaming data, APIs, and application services
- Familiarity with observability and operational tooling such as Azure Monitor, Application Insights, MLflow tracking, Datadog, or equivalent monitoring platforms
- 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
- Proven ability to provide technical guidance, review modeling approaches, and mentor other data scientists or ML engineers
- Strong communication and collaboration skills, with the ability to explain technical trade-offs and model outcomes to both technical and non-technical stakeholders
Preferred Qualifications
- Experience leading complex ML initiatives involving multiple models, cross-functional teams, and production operationalization
- Experience with time-series forecasting, optimization, recommender systems, anomaly detection, NLP, or domain-specific applied AI use cases
- Familiarity with LLM-assisted analytics, embeddings, retrieval-enhanced ML workflows, or hybrid ML + GenAI solution patterns
- Experience integrating ML solutions into enterprise applications, APIs, business workflows, and digital products
- Experience with feature stores, explainability frameworks, responsible AI toolkits, and model governance controls
- Familiarity with Azure OpenAI, Azure AI Studio, Azure AI Search, or equivalent platforms where ML and GenAI capabilities coexist
- Experience contributing reusable frameworks, MLOps standards, internal accelerators, or shared data science utilities
- Working knowledge of distributed training, large-scale data processing, and performance optimization in production environments
- Experience with advanced MLOps and platform tooling such as MLflow, Kubeflow, Azure ML, TensorBoard/ TensorFlow Extended, or equivalent ecosystem tools
- Experience operating in a build-own-operate product environment with strong expectations around reliability, observability, supportability, and continuous improvement
- Ability to influence architectural and platform decisions for ML-enabled products while remaining hands-on in model development and operationalization