Senior Data Scientist
Senior Data Scientist with 5+ years' experience designing, training, and deploying ML models into production at scale. Daily work spans end-to-end experiments and pipelines using Python, SQL, Pandas, Scikit-learn, TensorFlow/PyTorch, plus big data and cloud tooling like Spark, Databricks, Hadoop, Airflow, and AWS (EMR, SageMaker).
- Compensation
- Not disclosed
- Location
- India
- Work setup
- Remote
- Seniority
- Senior
- Employment
- Not specified
- Experience
- 5+ years
- Category
- Data science
- Posted
Skills
Must have
- Minimum 5 years of experience in Data Science or Applied Machine Learning
- Strong proficiency in Python, SQL, and ML libraries (Pandas, Scikit-learn, TensorFlow, PyTorch)
- Proven expertise in deploying ML models into production systems
- Experience with big data platforms (Hadoop, Spark) and distributed data processing
- Hands-on experience with Databricks, Airflow, and AWS EMR
- Strong knowledge of AWS cloud services (S3, Lambda, SageMaker, EC2, etc.)
- Solid understanding of query optimization, storage systems, and data pipelines
- Excellent problem-solving skills, with the ability to design scalable solutions
- Strong communication and collaboration skills for cross-functional teams
Full description
We’re seeking a highly skilled, execution-focused Senior Data Scientist with a minimum of 5 years of experience. This role demands hands-on expertise in building, deploying, and optimizing machine learning models at scale, while working with big data technologies and modern cloud platforms. You will be responsible for driving data-driven solutions from experimentation to production, leveraging advanced tools and frameworks across Python, SQL, Spark, and AWS. The role requires strong technical depth, problem-solving ability, and ownership in delivering business impact through data science.
Responsibilities
- Design, build, and deploy scalable machine learning models into production systems.
- Develop advanced analytics and predictive models using Python, SQL, and popular ML/DL frameworks (Pandas, Scikit-learn, TensorFlow, PyTorch).
- Leverage Databricks, Apache Spark, and Hadoop for large-scale data processing and model training.
- Implement workflows and pipelines using Airflow and AWS EMR for automation and orchestration.
- Collaborate with engineering teams to integrate models into cloud-based applications on AWS.
- Optimize query performance, storage usage, and data pipelines for efficiency.
- Conduct end-to-end experiments, including data preprocessing, feature engineering, model training, validation, and deployment.
- Drive initiatives independently with high ownership and accountability.
- Stay up to date with industry best practices in machine learning, big data, and cloud-native deployments.
Requirements
- Minimum 5 years of experience in Data Science or Applied Machine Learning.
- Strong proficiency in Python, SQL, and ML libraries (Pandas, Scikit-learn, TensorFlow, PyTorch).
- Proven expertise in deploying ML models into production systems.
- Experience with big data platforms (Hadoop, Spark) and distributed data processing.
- Hands-on experience with Databricks, Airflow, and AWS EMR.
- Strong knowledge of AWS cloud services (S3, Lambda, SageMaker, EC2, etc.).
- Solid understanding of query optimization, storage systems, and data pipelines.
- Excellent problem-solving skills, with the ability to design scalable solutions.
- Strong communication and collaboration skills to work in cross-functional teams.
Benefits
- Best-in-class salary: We hire strong talent and compensate accordingly.