- Proficient in programming languages such as Python or R.

- Expertise in statistics, machine learning algorithms, and data mining techniques.

- Experience with data preprocessing, feature engineering, and model validation.

- Skilled in data visualization tools and libraries (Tableau, Power BI, Matplotlib, Seaborn).

- Familiarity with big data platforms (Spark, Hadoop) and SQL databases.

- Knowledge of cloud-based ML platforms (AWS SageMaker, Azure ML, GCP AI Platform).

- Understanding of experimental design and A/B Testing.

- Ability to communicate complex technical concepts to non-technical stakeholder.

Key Responsibilities :

- Analyze large and diverse datasets to identify trends, patterns, and insights.

- Develop, train, and validate predictive and classification models.

- Analyze and select the appropriate AI foundation models for use where appropriate.

- Design experiments and conduct hypothesis testing to support business initiatives.

- Collaborate with data engineers to prepare and optimize data for analysis.

- Translate analytical findings into clear, actionable recommendations.

- Present insights and reports to stakeholders using visual storytelling techniques.

- Monitor and maintain models in production environments to ensure ongoing accuracy.

- Stay updated on emerging tools, techniques, and best practices in data science.

Preferred Qualifications :

- Bachelors or Masters degree in Computer Science, Data Science, Engineering, or related field.

- 4+ years of experience in data engineering, data science, or related domains.

- Hands-on experience with SQL, Python, and distributed data systems.

- Knowledge of machine learning techniques and statistical analysis.

- Experience with cloud data platforms (Azure Data Factory, AWS Glue, GCP BigQuery).

- Familiarity with DevOps practices and CI/CD for data pipelines.

Platforms & Operations Experience (Preferred) :

- Experience working with Azure, AWS, or Google Cloud data tools.

- Operational experience with data orchestration tools (Airflow, ADF, Glue).

- Understanding of Kubernetes, Docker, or containerized environments.

- Hands-on experience with data warehousing platforms (Snowflake, Redshift, BigQuery).

- Experience in monitoring, logging, and alerting operations for data workflows.