About the Role:

We are looking for an experienced Senior Data Scientist to design, develop, deploy, and optimize machine learning solutions that solve complex business problems. The role will involve working across Python, SQL, statistics, experimentation, machine learning, NLP/forecasting, data pipelines, production ML, model monitoring, and MLOps, while partnering closely with business and technology stakeholders.

The ideal candidate should be able to take data science initiatives from problem definition and experimentation through model development, productionization, monitoring, and continuous improvement, with a strong focus on measurable business outcomes.

Key Responsibilities:

- Define and solve complex business problems using data science, statistical modelling, and machine learning.

- Develop, train, validate, and optimize machine learning models using Python and SQL.

- Perform exploratory data analysis, feature engineering, statistical analysis, and model evaluation.

- Design and analyze A/B tests, experiments, and hypothesis-driven studies to measure product and business impact.

- Apply appropriate techniques for NLP, forecasting, classification, regression, clustering, and other machine learning use cases based on business requirements.

- Build and maintain data preparation workflows, ETL processes, and data pipelines required for analytical and ML workloads.

- Collaborate with Data Engineers to ensure availability, quality, consistency, and reliability of data used for modelling.

- Productionize machine learning models and support deployment into scalable production environments.

- Implement model monitoring, performance tracking, drift detection, and model health checks.

- Contribute to MLOps practices, including model versioning, reproducibility, deployment automation, testing, and lifecycle management.

- Analyze model performance and continuously improve accuracy, stability, scalability, and business relevance.

- Translate analytical findings and model outputs into clear, actionable business recommendations.

- Partner with Product, Engineering, Business, and other cross-functional teams to understand requirements and prioritize data science initiatives.

- Present analytical findings, experiment results, model performance, and business insights to technical and non-technical stakeholders.

- Maintain documentation covering data sources, modelling approaches, experiments, assumptions, performance, and production processes.

- Stay current with developments in machine learning, data science, MLOps, and applied AI and identify relevant opportunities for the organization.

Required Skills & Experience:

- 5 - 10 years of experience in Data Science, Machine Learning, Advanced Analytics, or a closely related field.

- Strong hands-on proficiency in Python and SQL.

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

- Hands-on experience developing and evaluating machine learning models.

- Experience with A/B testing, experimentation, and statistical significance analysis.

- Practical experience in one or more areas such as NLP, time-series forecasting, classification, regression, or customer modelling.

- Experience building or working with ETL processes, data pipelines, and large datasets.

- Strong understanding of production ML, model deployment, monitoring, and lifecycle management.

- Experience with MLOps practices and tools is preferred.

- Familiarity with at least one major cloud platform such as AWS, Azure, or GCP.

- Good understanding of machine learning frameworks such as scikit-learn, PyTorch, TensorFlow, or equivalent.

- Strong analytical, problem-solving, and data interpretation skills.

- Ability to connect technical analysis with business impact and measurable outcomes.

- Strong communication, presentation, and stakeholder-management skills.

- Ability to work independently while collaborating effectively with cross-functional teams.