Data Scientist

About Third Bridge

Third Bridge is a leading global research firm established in 2007, with a team of over 1,500 employees worldwide dedicated to fueling decisions with expert insights. We accelerate and enhance decision-making for investors and business leaders by unearthing unique expert insights across multiple sectors, geographies, and topics. For nearly 20 years, we’ve helped clients access knowledge on demand from experts, in-person and through our Library covering over 65,000 companies. We stand at the cutting edge of technology and investment research, driving innovation to deliver solutions that set new industry standards. We are building a Data Science capability within our Data Architecture function, and are seeking a Data Scientist who combines technical rigour with a prototyping mindset — someone dedicated to proving what’s possible with our data and handing those proofs to engineering.

About the Role

As a Data Scientist at Third Bridge, you will be the engineering-focused prototyping engine within a high-impact data team. Working alongside our senior analytics capability and reporting to the Principal Data Architect, your primary mandate is to design, build, and validate proof-of-concept (PoC) solutions — from new dataset pipelines and ML models to AI-powered internal tools — and hand off successful proofs to the engineering team for production integration. You will work with Third Bridge’s rich proprietary data assets: transcripts, events, interaction data, commercial performance metrics, and content derived from tens of thousands of expert interviews. The role gives you the mandate to find new ways to extract value from that data, with the freedom to experiment and the expectation to ship.

Key Responsibilities

• Design and build PoC solutions with a clear ‘ship-or-kill’ decision framework, defining evaluation criteria upfront so success is measurable, not just qualitative.
• Build extraction, transformation, and loading (ETL/ELT) pipelines to create net-new datasets for analytical or ML use, including feature engineering pipelines for model training and downstream reporting.
• Apply supervised and unsupervised ML techniques to commercially relevant problems: usage propensity, client segmentation, churn modelling, content recommendation, and anomaly detection.
• Develop lightweight Python-based tools and notebook applications that allow business stakeholders to interact with model outputs or curated data extracts.
• Prototype AI-assisted internal tools — including applications leveraging LLM APIs, embedding-based search, and retrieval-augmented generation (RAG) — to demonstrate near-term business value from Third Bridge’s content assets.
• Collaborate closely with the analytics peers to align on data definitions, shared datasets, and metric standards that underpin both experimental and production work.
• Work with the engineering team to define production requirements for successful PoCs, producing clean, well-documented code and architecture notes to support seamless handoff.
• Advocate upstream for data quality and instrumentation requirements with Product and Engineering, contributing to the team’s engineering standards and practices.

Essential Qualifications

• Masters degree (or equivalent) in Computer Science, Data Science, Statistics, Mathematics, Engineering, or a quantitative field such as Economics.
• Professional experience in a data science, ML engineering, or applied data role.
• Strong Python programming skills, including hands-on experience with pandas, scikit-learn, and at least one ML or deep learning framework.
• Working knowledge of Bedrock and other AWS managed services.
• Demonstrable experience building and deploying at least one end-to-end ML model or data pipeline in a professional setting, with clear evaluation criteria and documented outcomes.
• A track record of building PoC or prototype solutions and iterating toward production-readiness, with the discipline to define ‘ship-or-kill’ criteria upfront.
• Strong version control practices (Git) and familiarity with software engineering workflows including code review and CI/CD awareness.
• The ability to communicate technical findings and trade-offs clearly to both technical peers and non-technical stakeholders.

Desired Attributes

• Exceptional problem-solving instincts and a bias toward shipping: able to make a PoC useful and demonstrable quickly, without over-engineering early-stage solutions.
• Familiarity with AWS Bedrock or other managed AI/LLM platforms (e.g. OpenAI API, Anthropic API) for building and integrating AI-powered features is a strong plus, though AI capability should complement, not replace, a solid foundation in traditional ML.
• Experience with NLP, text classification, embedding models, or semantic search, particularly applied to content-rich or document-heavy datasets.
• Familiarity with building lightweight web applications or data tools such as Streamlit, FastAPI, or Flask, enabling business stakeholders to interact directly with model outputs.
• Experience with orchestration or transformation tooling such as dbt, Airflow, or Prefect, demonstrating an ability to build reproducible and maintainable pipelines.
• A tech-agnostic mindset, open to selecting the right tool for each problem — whether that is a classical statistical model, a gradient boosting approach, or an LLM-backed workflow.
• Exposure to product analytics, B2B SaaS, publishing, or content-rich datasets is a plus.
• Natural curiosity and a proactive approach to staying current with developments in both traditional ML and generative AI — and bringing relevant ideas back to the team.
• Excellent collaboration skills, with the instinct to treat the analytics peer and engineering team as partners rather than handoff recipients.