Senior Data Scientist

Responsibilities

• Design, develop, and deploy machine learning models for predictive analytics, classification, regression, clustering, and forecasting applications.
• Build scalable machine learning pipelines using Python, PySpark, and cloud-based technologies.
• Perform feature engineering, model evaluation, and optimization using structured and unstructured data.
• Develop production-ready AI and machine learning solutions that integrate with enterprise software platforms.
• Partner with Product Management, Engineering, and business stakeholders to translate business challenges into data-driven solutions.
• Analyze large datasets to identify trends, patterns, and opportunities that improve operational performance and customer outcomes.
• Develop dashboards, visualizations, and analytical tools that communicate insights to technical and non-technical audiences.
• Evaluate emerging machine learning and AI technologies and recommend improvements to existing solutions.
• Support production model monitoring, performance measurement, and continuous model improvement.
• Mentor junior team members and contribute to a collaborative, innovation-driven environment.

• Bachelor's degree in Computer Science, Data Science, Statistics, Mathematics, Engineering, or a related discipline.
• 5+ years of professional experience developing machine learning solutions in production environments.
• Strong experience with Python and PySpark.
• Hands‑on experience with Azure Databricks or similar large‑scale distributed data processing platforms.
• Experience building and deploying end‑to‑end machine learning solutions.
• Strong understanding of statistical modelling, regression, classification, clustering, and predictive analytics.
• Experience with time‑series forecasting is highly desirable.
• Familiarity with Tensor Flow, scikit‑learn, MLflow, Spark MLlib, or similar machine learning frameworks.
• Experience working with cloud platforms such as Azure, AWS, or Google Cloud Platform.
• Strong SQL and data engineering skills.
• Excellent communication skills with the ability to explain technical concepts to business stakeholders.

• Experience with Natural Language Processing (NLP) or Generative AI.
• Experience supporting production MLOps environments, CI/CD, and model monitoring.
• Background working with large‑scale enterprise or IoT datasets.
• Experience collaborating across Engineering, Product Management, and Analytics teams.

• Join a global technology organization investing heavily in AI and machine learning.
• Work on meaningful, production‑scale machine learning initiatives.
• Collaborate with experienced engineering, product, and analytics teams.
• Competitive compensation, annual bonus, and comprehensive benefits.
• Hybrid work environment with opportunities for continued professional growth.

Compensation:$145,000–$150,000 USD + Bonus