Netrolynx AI
Data Scientist
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About The Company
Third Bridge Group is a premier global research firm founded in 2007, with a dedicated team of over 1,500 professionals worldwide. Our core mission is to fuel decision-making processes for investors and business leaders by providing access to expert insights across various sectors, geographies, and topics. We specialize in uncovering unique, actionable knowledge through a combination of in-person interviews, digital content, and our extensive library covering more than 65,000 companies. Our commitment to innovation and excellence has positioned us at the forefront of the industry, leveraging cutting-edge technology and advanced research methodologies to deliver high-value solutions. As part of our growth strategy, we are expanding our Data Science capabilities within the Data Architecture function, seeking talented individuals who are passionate about turning data into strategic assets.
About The Role
As a Data Scientist at Third Bridge, you will play a pivotal role within our high-impact data team, focusing on engineering-driven prototyping and proof-of-concept development. Reporting directly to the Principal Data Architect and collaborating closely with our analytics and engineering teams, your primary responsibility will be to design, build, and validate innovative data solutions that demonstrate potential for production deployment. These solutions encompass new dataset pipelines, machine learning models, and AI-powered internal tools aimed at extracting maximum value from our proprietary data assets, which include transcripts, events, interaction logs, commercial performance metrics, and content derived from extensive expert interviews.
Your work will involve experimenting with various data extraction, transformation, and loading (ETL/ELT) processes, applying advanced supervised and unsupervised machine learning techniques to solve commercially relevant problems such as user propensity, client segmentation, churn prediction, content recommendation, and anomaly detection. Additionally, you will develop lightweight Python-based tools and interactive notebooks to enable stakeholders to explore model outputs and curated data. A key aspect of your role will be prototyping AI-driven internal applications that leverage large language models (LLMs), embedding-based search, and retrieval-augmented generation (RAG) techniques to demonstrate immediate business value. You will also work closely with the engineering team to ensure seamless handoff of successful prototypes for production, emphasizing well-documented, scalable, and maintainable solutions.
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This role offers the freedom to explore new data-driven approaches and experiment with emerging AI technologies, fostering a culture of innovation while maintaining a focus on delivering tangible results. Your efforts will directly impact our ability to unlock insights from complex datasets and develop solutions that support strategic decision-making across the organization.
Qualifications
The ideal candidate will possess a master's degree (or equivalent) in Computer Science, Data Science, Statistics, Mathematics, Engineering, Economics, or a related quantitative discipline. You should have professional experience in data science, machine learning engineering, or applied data roles, with demonstrated success in building and deploying end-to-end ML models or data pipelines. Strong Python programming skills are essential, including proficiency with pandas, scikit-learn, and at least one deep learning framework such as TensorFlow or PyTorch. Familiarity with AWS managed services, particularly Bedrock, and experience in developing scalable data solutions are highly desirable.
You must have a proven track record of creating proof-of-concept solutions that have progressed toward production, with clearly defined evaluation criteria and documented outcomes. Knowledge of version control systems like Git, along with experience in software engineering workflows, including CI/CD practices, is required. Excellent communication skills are necessary to articulate technical findings effectively to both technical and non-technical stakeholders, ensuring alignment and understanding across teams.
Responsibilities
- Design and develop proof-of-concept solutions with clear success metrics and decision frameworks, ensuring rapid iteration and demonstrability.
- Create robust ETL/ELT pipelines to generate new datasets for analytical and machine learning purposes, incorporating feature engineering and data validation steps.
- Apply supervised and unsupervised machine learning techniques to address key business challenges, including customer segmentation, churn prediction, and anomaly detection.
- Develop interactive Python tools and notebooks to facilitate stakeholder engagement with data insights and model outputs.
- Prototype AI-powered internal applications utilizing LLM APIs, embedding models, and retrieval-augmented generation techniques to showcase immediate business applications.
- Collaborate with analytics teams to establish consistent data definitions, shared datasets, and standard metrics that underpin experimental and operational work.
- Work with engineering teams to define production requirements, ensuring prototypes are scalable, maintainable, and well-documented for seamless deployment.
- Advocate for data quality improvements and instrumentation needs, contributing to the development of engineering standards and best practices.
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Benefits
- Competitive vacation allowance starting at 25 days, increasing to 28 days after two years, plus UK Bank Holidays.
- Annual personal development budget of £1000 to support continuous learning and skill enhancement.
- Comprehensive health and wellbeing benefits, including private medical insurance, healthcare cash plans, and mental health initiatives.
- Future planning and security features such as pension contributions starting at 4% (with increases over time) and life insurance coverage of four times your base salary.
- Flexible working arrangements, including the option to work remotely for up to one month per year, along with volunteer days, personal days, and summer Fridays.
- Recognition programs offering points redeemable for hotel stays, gift cards, charitable donations, and more.
- Engagement in social activities, including team gatherings, daily breakfast, and networking events.
- Commitment to ESG principles through CSR initiatives, environmental sustainability, and diversity and inclusion programs such as
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