Data Scientist - ML Operations

As a Data Scientist in our Machine Learning Operations, you will play a crucial role in ensuring that our machine learning models are scalable, reliable, and efficient. You will bridge the gap between data science and production, building and optimizing ML pipelines, automating workflows, and ensuring seamless deployment, monitoring, and retraining of models. Working closely with data scientists, engineers, and product teams, you will contribute to innovative ML solutions that enhance customer experiences and drive business impact.

What you’ll you be doing:

• Data Engineering & ML Pipeline Optimization

  • Design and implement scalable, efficient, and automated ML pipelines for data ingestion, feature engineering, model training, and deployment.

  • Utilize tools like SQL, Python, Spark, Snowflake, Airflow, and DBT for data preprocessing, transformation, and orchestration.

  • Develop and maintain robust microservices to support ML-driven product features.

  • Work with relational databases, ensuring data quality, integrity, and accessibility for ML workflows.

  • Implement real-time data processing pipelines using Kafka and AWS Lambda.

  Machine Learning Deployment & Performance Monitoring

  • Build and optimize ML models for scalability, leveraging AWS SageMaker, Databricks, and Docker.

  • Continuously monitor model performance, detect anomalies, and automate model retraining.

  • Establish best practices in model governance, scalability, and compliance.

  • Collaborate with MLOps engineers to integrate CI/CD pipelines for seamless model deployment.

  Collaboration & Innovation

  • Work closely with data scientists, software engineers, and product teams to translate business needs into impactful ML solutions.

  • Communicate complex ML findings in a clear and actionable manner to stakeholders.

  • Stay up-to-date with emerging ML and MLOps technologies, contributing to continuous improvement initiatives.

We're looking for someone with:

• • Master’s or Bachelor’s degree in Data Science, Computer Science, Mathematics, or a related field.

  • At least 3 years of industry experience in data science, machine learning, or MLOps.

  • Strong proficiency in Python (Scikit-learn, Pandas, NumPy), SQL, and Spark.

  • Hands-on experience with MLFlow, Airflow, ZenML, SageMaker Pipelines, or similar tools.

  • Proficiency in Snowflake, AWS services (EC2, EKS, Cognito, CloudFormation), and Kafka.

  • Ability to break down complex ML projects into clear, achievable deliverables.

   

Bonus points for:

• Experience with ML development and deployment tools such as ZenML, MLFlow, Airflow, …
• Experience with AWS Services such as EC2, EKS, Cloudformation, Cognito, …
• Experience with Kafka