Senior Data Scientist

Summary:

In this role, you will design and maintain scalable, robust data pipelines, handle large-scale data ingestion, and design workflows using Databricks, PySpark, Python, and SQL, leveraging AWS and other cloud technologies. You will work across the full data lifecycle — from raw data processing to developing, deploying, and monitoring data-driven models, that help enable predictive insights that directly impact business strategy. This is an ideal opportunity for anyone who is passionate about hands-on data engineering, enjoys working across the end-to-end data lifecycle, and thrives in a collaborative, cloud-first environment. This is an ideal role for someone with strong programming and cloud skills, data engineering expertise, and a passion for data science and analytics.

Key Responsibilities

1. Data Engineering & Pipeline Management

• Design, implement, and maintain robust ETL/ELT pipelines for ingesting structured and unstructured data from multiple sources.
• Ensure data quality, consistency, and availability for downstream analytics and modelling.
• Leverage tools like Databricks for data warehousing.

1. Infrastructure & Cloud Management

• Manage cloud-based data infrastructure (GCP, AWS, or Azure) with an emphasis on scalability, cost optimization, and reliability.
• Apply Infrastructure as Code (IaC) principles using tools like Terraform or CloudFormation.

1. Data Modelling & Feature Engineering

• Build and maintain logical and physical data models for analytics and ML workloads.

• Perform feature engineering, data transformations, and statistical preprocessing to prepare datasets for modelling.

1. Machine Learning & Advanced Analytics

• Understanding of machine learning and predictive models using Python (scikit-learn, TensorFlow, PyTorch) or R from a data engineering perspective.
• Understanding of statistical modelling, time-series forecasting, NLP, or computer vision techniques as needed from a data engineering perspective.

1. Production & Monitoring

• Deploy models into production pipelines and monitor their performance, accuracy, and drift.
• Ensure models and data pipelines are scalable, maintainable, and automated.

1. Collaboration & Mentorship

• Work closely with data analysts and business stakeholders to define requirements and deliver impactful solutions.
• Mentor colleagues on best practices in  data engineering, and analytics engineering.

Skills & Qualifications

Required:

• 4–6 years of experience in data engineering, data Science, and statistics roles.
• Bachelor’s or Master’s degree in Engineering.
• Strong programming skills in Python, including PySpark for distributed data processing.
• Advanced SQL expertise and experience designing data models for analytics and ML.
• Experience in Javascript, HTML, Appscript 
• Solid grounding in software engineering best practices — version control, testing, and automation.
• Hands-on experience with Databricks for pipeline development and data transformation.
• Proven experience with Cloud data services (Preferably AWS).
• Strong understanding of ETL/ELT design patterns, data orchestration, and modular code principles.
• Experience deploying and maintaining data pipelines and machine learning models in production.
• Cost Optimization and management of Data Infrastructure.

Preferred:

• Experience parsing and integrating data from GraphQL APIs.
• Familiarity with Terraform, CloudFormation, or containerization (Docker/Kubernetes).
• Exposure to data visualization tools like Tableau, Power BI, or Looker Studio.