Job Description

We are looking for an experienced Data Scientist. The candidate should have expertise in
building machine learning and econometrics models, proficiency in Python and PySpark, and experience
working with AWS cloud infrastructure, including SageMaker. The role will also require an expert knowledge of
MLOps practices, good client management skills, and collaboration with cross-functional teams. While
forecasting expertise is a valuable skill, it will be a secondary focus in this position.
Key Responsibilities:

• Design, develop, and deploy machine learning and statistical models to solve complex business

problems.

• Collaborate with cross-functional teams (data engineering, ML engineering, and business) to gather and

understand requirements.

• Implement and scale machine learning models using Python and PySpark, optimizing performance and

accuracy.

• Utilize AWS services (including SageMaker) for deploying, training, and managing models in the cloud.
• Apply MLOps best practices to ensure scalability, reliability, and maintainability of ML pipelines.
• Support models in production, define standards and processes for implementing MLOps in the

organization, for, but not limited to, automating ML operations, track and fix model and data drifts,
automated deployment, set up checks to identify and prevent model deterioration over time

• Recommend multiple ways to orchestrate and score the deployed models within the AWS cloud suite

and execute the best way to run the models based on the organization requirements.

• Implement methods to invoke the developed models and integrate into existing applications
• Develop and implement forecasting models as needed, but primary focus will be on other types of

machine learning and statistical time series models.

• Work closely with business stakeholders to translate business needs into technical solutions.

Required Skills & Experience:

• 4-5 years of experience in data science, with a strong focus on machine learning and statistical model

development.

• Proficiency in Python and PySpark for large-scale data processing and modeling.
• Hands-on experience with AWS cloud services, especially SageMaker for building and deploying

models.

• Familiarity with MLOps tools and best practices for managing machine learning pipelines including

CI/CD pipelines, orchestration through AWS suite of products, including scripts.

• Demonstrated ability to develop accurate forecasting models (though this will be a secondary skill).
• Strong communication skills with the ability to manage client relationships and expectations effectively.
• Experience working in collaborative, cross-functional teams, including engineers, product managers,

and business stakeholders.Preferred Qualifications:

• Experience with big data tools like Spark.
• Exposure to advanced machine learning techniques (e.g., deep learning, reinforcement learning).

Ciel HR