Senior Data Scientist - 6-month contract

Chambers & Partners; Greater London; On-site; GBP 70,000 - 100,000; Full time

Job summary

A leading company in the legal sector is seeking a Senior Data Scientist to drive the development of advanced analytics and AI/ML solutions. The role involves managing data science projects, collaborating with cross-functional teams, and delivering impactful results within a 6-month contract. Ideal candidates are proactive, have strong analytical skills, and a proven track record in advanced analytics.

Qualifications

• Proficient in Python or R with experience in machine learning algorithms.
• Experience in cloud services, particularly Azure.
• Strong background in statistics and data analysis.

Responsibilities

• Lead and execute complex data science projects using cloud tools.
• Develop and deploy machine learning models on cloud platforms.
• Collaborate with teams to create data-driven products.

Skills

Python

R

Advanced Statistics

Data Science Methods

Problem Solving

Communication

Education

Bachelor's degree in Data Science, Computer Science, Statistics

Master's degree or PhD (related field)

Tools

AzureML Studio

Azure Databricks

MLflow

Job description

Overview

We’re seeking a Senior Data Scientist to lead the development of advanced analytics and AI/ML solutions that unlock real value across our business. This is a contract role for 6 months.

In this contract role, you'll work with proprietary and B2B research datasets to design, deliver, and scale data-driven products. Collaborating closely with teams in Product, Research, and Technology, you'll help turn strategic ideas into working MVPs—ensuring high standards of methodology, quality, and business relevance throughout.

You’ll also help shape the data science environment by working alongside our tech teams to support a robust and flexible infrastructure, including sandbox environments for onboarding and evaluating new data sources.

This is a great opportunity for a self-driven, impact-oriented data scientist who thrives in a fast-paced, cross-functional setting—and is eager to deliver meaningful results in a short time frame.

Main Duties and Responsibilities

1. Spearhead and execute complex data science projects using a combination of open-source and cloud tools, driving innovation and delivering actionable insights.
2. Develop and deploy advanced machine learning models using cloud-based platforms.
3. Collaborate with product managers and designers to ensure the feasibility of product extensions and new products based on existing proprietary, quantitative, and qualitative datasets.
4. Work with outputs from Research and historical data to identify consistent and inconsistent product features and document precise requirements for improved consistency.
5. Collaborate with designers, Tech colleagues, and expert users to come up with engaging ways to visualize data and outliers/exceptions for non-technical audiences.
6. Design and develop novel ways to showcase and highlight key analysis from complex datasets, including joining across datasets which do not perfectly match.
7. Collaborate with Product, Tech, Research, and other stakeholders to understand and define a new, marketable product from existing data.
8. Create and present progress reports and ad-hoc reviews to key stakeholders and teams.
9. Constantly think about and explain to stakeholders how analytics “product” could be refined and productionized in the future.
10. Work with Tech colleagues to improve the Data Science workspace, including providing requirements for Data Lake, Data Pipeline, and Data Engineering teams.
11. Expand upon the tools and techniques that have already been developed.
12. Help us understand our customers (both internal and external) better so we can provide the right solutions to the right people, including proactively suggesting solutions for nebulous problems.
13. Be responsible for the end-to-end Data Science lifecycle: investigation of data, from data cleaning to extracting insights and recommending production approaches.
14. Responsible for demonstrating value addition to stakeholders.
15. Coach, guide, and nurture talent within the data science team, fostering growth and skill development.

Skills and Experience

• Delivering significant and valuable advanced analytics projects and/or assets in industry and/or professional services.
• Proficiency in programming languages such as Python or R, and extensive experience with LLMs, ML algorithms, and models.
• Experience with cloud services like AzureML Studio, Azure Functions, Azure Pipelines, MLflow, Azure Databricks, etc., is a plus.
• Experience working in Azure/Microsoft environments is considered a real plus.
• Proven understanding of data science methods used for analyzing and making sense of research data outputs and survey datasets.
• Fluency in advanced statistics with a solid grounding through education and experience.

Person Specification

• Bachelor's, Master's, or PhD degree in Data Science, Computer Science, Statistics, or a related field.
• Comfortable working with uncertainty and ambiguity, from initial concepts through iterations and experiments to find the right products/services to launch.
• Excellent problem-solving and strong analytical skills.
• Proven aptitude to learn new tools, technologies, and methodologies.
• Understanding of requirements for software engineering and data governance in data science.
• Proven ability to manage and mentor data science teams.
• Evidence of taking a company or department from Analytics to Data Science to AI and ML deployed at scale.
• Ability to translate complex analysis findings into clear narratives and actionable insights.
• Excellent communication skills, with the ability to listen and collaborate with non-technical and non-quantitative stakeholders.
• Experience working with client-facing and tech teams to ensure the right data is collected with sufficient quality and stored in an easy-to-examine reporting format.
• Experience presenting investigations and insights to audiences with varying skill sets and backgrounds.
• Nice to have: experience working with market research methods and datasets.
• Nice to have: experience in the professional services or legal sector.
• B2B market research experience would be a significant plus.