Description

Job Summary

We are seeking a highly skilled Data Scientist with strong experience in cloud-based data science and AI solutions, including Microsoft Copilot and or Azure AI services, and/or AWS analytics and ML platforms. The ideal candidate will design, build, and deploy data-driven solutions that leverage large-scale datasets, machine learning models, and generative AI to drive business insights and automation.

You will collaborate closely with product, engineering, and business stakeholders to translate complex problems into scalable, cloud-native analytics and AI solutions.

Key Responsibilities

Data Science & Analytics

• Develop, validate, and deploy machine learning and statistical models for prediction, classification, recommendation, and optimization.
• Perform exploratory data analysis (EDA) on structured and unstructured data to identify trends, patterns, and anomalies.
• Design and maintain end-to-end data science pipelines, from data ingestion and feature engineering to model deployment and monitoring.
• Communicate insights and model results to technical and non-technical stakeholders using data visualizations and clear narratives.

Generative AI & Microsoft Copilot

• Design and integrate Microsoft Copilot–based solutions to enhance productivity, decision-making, and automation.
• Develop prompt engineering strategies, retrieval-augmented generation (RAG) workflows, and custom Copilot extensions.
• Leverage Azure OpenAI, Microsoft Fabric, Power Platform, and Copilot Studio to build enterprise-grade AI applications.
• Ensure responsible AI practices, including model explainability, bias mitigation, and data privacy.

Cloud & MLOps

• Build and deploy models using cloud-native services such as:
  • Azure: Azure ML, Synapse Analytics, Data Factory, Fabric
  • AWS: SageMaker, Glue, Athena, Redshift, Lambda
• Implement MLOps best practices, including CI/CD for models, versioning, automated retraining, and performance monitoring.
• Optimize model performance, scalability, and cost in cloud environments.

Collaboration & Stakeholder Engagement

• Partner with data engineers to define data architecture and pipelines.
• Work with product managers and business leaders to translate requirements into analytical solutions.
• Mentor junior data scientists and contribute to data science standards and best practices.

Required Qualifications

• Bachelor’s or Master’s degree in Data Science, Computer Science, Statistics, Mathematics, or a related field.
• 3–7+ years of experience in data science, machine learning, or applied analytics.
• Strong proficiency in Python (NumPy, Pandas, Scikit-learn) and/or R.
• Experience with SQL and working with large-scale datasets.
• Hands-on experience with Microsoft Copilot/ Azure AI services, or AWS ML/analytics tools.
• Solid understanding of machine learning algorithms, statistical modeling, and data validation techniques.
• Experience deploying models in production cloud environments.

Preferred Qualifications

• Experience with generative AI, LLMs, and prompt engineering.
• Familiarity with Azure OpenAI Service or AWS Bedrock.
• Knowledge of Power BI, Tableau, or other data visualization tools.
• Experience with Docker, Kubernetes, or serverless architectures.
• Understanding of data governance, security, and compliance in cloud platforms.
• Certifications such as Azure Data Scientist Associate, AWS Certified Machine Learning – Specialty, or similar.

Key Skills

• Machine Learning & Statistical Modeling
• Generative AI & Copilot Development
• Cloud Computing (Azure / AWS)
• Python, SQL, Data Visualization
• MLOps & Model Deployment
• Business Communication & Storytelling

Key Skills

Education

Any Graduate