Data Engineer/Data Scientist

Role Overview  The Data Engineer / Data Scientist will be responsible for designing, developing, and maintaining data pipelines, performing data analysis, building predictive models, and ensuring data quality across platforms and operations.  Responsible for extracting insights and value from complex datasets by applying statistical analysis, machine learning, and data visualization techniques. The role involves collaborating with business stakeholders to understand problems, developing predictive and prescriptive models, and communicating findings to support data-driven decision-making. Data Scientists work closely with data engineers and AI teams to operationalize models and ensure actionable outcomes.  Key Responsibilities  - Design, build, and optimize scalable data pipelines and ETL processes.  - Develop and maintain data models, data marts, and analytical datasets.  - Collaborate with cross-functional teams to gather requirements and deliver data-driven solutions.  - Perform exploratory data analysis (EDA) and create machine learning models as required.  - Implement data quality checks, validation rules, and monitoring processes.  - Automate data workflows and ensure timely availability of data.  - Work with cloud platforms such as Azure, AWS, or GCP for data engineering activities.  Technical Skills  - 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 stakeholders  Key Responsibilities (non-exhaustive)  - 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  Quality Testing Responsibilities  - Develop and execute data validation and data quality test cases.  - Perform unit and integration testing for data pipelines.  - Monitor data accuracy, completeness, and consistency across systems.  - Identify data anomalies and work with engineering teams to resolve issues.  - Document test results and maintain testing logs.  Preferred Qualifications  - Bachelor’s or Master’s degree in Computer Science, Data Science, Engineering, or related field.  - 3–5 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.