The Senior Data Scientist has a strong foundation in modeling, engineering and operations to build scalable solutions. Reporting to the Director of AI Engineering, the incumbent will play a crucial role in developing new models and solutions that align with our strategic objectives and business goals.

• Architect & Develop: Lead the design, development, and maintenance of advanced models and foundational data pipelines. Create modular, reusable components and libraries that enhance the efficiency and scalability of our modeling process.
• Technical Leadership: Mentor fellow data scientists and engineers by encouraging best practices for code structure, version control, CI/CD, testing, and reproducibility. Review pull requests and champion code quality across the data team.
• Cross-Functional Collaboration: Partner with product management, and engineering teams to translate complex models and analytical tools into scalable, real-world applications that add measurable business value.
• End-to-End Ownership: Manage the full project lifecycle—from requirements gathering and architecture to deployment and monitoring—ensuring solutions are robust and optimized for performance in cloud-based environments.
• Infrastructure & Tooling: Lead efforts to optimize and automate cloud-based data science environments, establishing guidelines for resource utilization, environment provisioning, and production deployments.
• Innovation & Continuous Improvement: Stay current with emerging technologies in AI, data engineering, MLOps, and machine learning. Continuously evaluate and integrate new techniques to maintain a competitive edge.

Salaries vary by location and are based on numerous factors, including, but not limited to, the relevant market, skills, experience, and education of the selected candidate. If an estimated salary range for this role is available, it will be provided in our Target Salary Range section. Our compensation package also includes bonus eligibility and a comprehensive benefits program. Benefits information can be found at Sidley.com/Benefits.

To perform this job successfully, an individual must be able to perform the Duties and Responsibilities (Duties) above satisfactorily and meet the requirements below. The requirements listed below are representative of the minimum knowledge, skill, and/or ability required. Reasonable accommodations will be made to enable individuals with disabilities to perform the essential functions of the job. If you need such an accommodation, please email staffrecruiting@sidley.com (current employees should contact Human Resources). 

Education and/or Experience:

Required:

• Master’s degree in Computer Science, Statistics or quantitative field
• A minimum of 5 years of experience in data science and/or data engineering, with a track record of building and maintain complex data pipelines or libraries
• Strong Python and SQL skills, especially in cloud-based environments (AWS, Azure, GCP). Familiarity with software engineering best practices (version control, code reviews, CI/CD, containerization) is critical.
• Deep knowledge of machine learning algorithms and data science methodologies, with an ability to deploy models in a production setting.
• Proven ability to learn new technical applications quickly.
• Strong Analytical ability.
• Demonstrated passion for shipping high-quality models, tools, and features frequently, maintaining a culture of agility and reliability.

Preferred:

• PhD in Computer Science, Statistics or quantitative field

Other Skills and Abilities:

The following will also be required of the successful candidate:

• Strong organizational skills
• Strong attention to detail
• Good judgment
• Strong interpersonal communication skills
• Strong analytical and problem-solving skills
• Able to work harmoniously and effectively with others
• Able to preserve confidentiality and exercise discretion
• Able to work under pressure
• Able to manage multiple projects with competing deadlines and priorities

Sidley Austin LLP is an Equal Opportunity Employer

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