Data Scientist

Synechron
City of London
1 month ago
Applications closed

Related Jobs

View all jobs

Data Scientist

Data Scientist

Data Scientist

Data Scientist

Data Scientist (Government)

Data Scientist - Renewable Energy

Synechron UK is seeking highly experienced Senior Data Scientists to lead innovative initiatives within our Credit Risk Technology team. This critical role offers the opportunity to shape the future of data and AI products through the strategic development of cutting-edge Machine Learning (ML) and Generative AI (Gen AI) solutions.



This role focuses on two key pathways:

  • Machine Learning (ML): Specialising in advanced ML algorithms and MLOps practices.
  • Generative AI (Gen AI): Focusing on deploying Retrieval-Augmented Generation (RAG) workflows and managing Large Language Model (LLM) applications.


What You’ll Do

As a Senior Data Scientist at Synechron, you will operate as a vital contributor, overseeing the full lifecycle of Data and AI products within a dynamic financial environment. Your core responsibilities include:


  • Advanced Data Analysis & Modeling: Uncover hidden patterns and develop insights pertinent to Credit Risk via sophisticated statistical, quantitative, and machine learning techniques.
  • Generative AI Applications: Leverage unstructured data, NLP, and LLMs to automate data lineage modeling and documentation processes.
  • Model Optimisation & Monitoring: Enhance model performance, detect anomalies, conduct variance and time-series analysis, and develop automated solutions for model monitoring—covering data drift and correction strategies.
  • Explainable AI for Risk: Apply advanced analytics to interpret root causes behind changes in pre-settlement risk.
  • Framework Development: Build scalable, reusable data science frameworks enabling efficient deployment of financial models.
  • Strategic Product Leadership: Lead end-to-end product lifecycle—from ideation and requirements to development, launch, and post-launch performance management.
  • Stakeholder Collaboration: Partner with senior leadership, business units, tech teams, risk, compliance, and operations to gather requirements, align priorities, and drive consensus.
  • Responsible AI & Governance: Ensure your solutions comply with strict risk, privacy, and ethical standards, including data governance and fairness in highly regulated financial settings.


Key Technologies & Tools

  • Core ML: scikit-learn, PyTorch, TensorFlow, XGBoost, LightGBM
  • Data Manipulation: Pandas, NumPy, LangGraph
  • MLOps & Orchestration: MLflow, Apache Airflow
  • Databases: Neo4j, MongoDB
  • API Development: FastAPI
  • Big Data (Beneficial): Apache Hadoop, Apache Spark (PySpark)


What We Need From You

  • 5-8 years of hands-on experience developing and deploying ML and/or Gen AI solutions in production.
  • Deep expertise in quantitative methods, probability, statistics, and numerical computing.
  • Proven MLOps proficiency—model deployment, versioning, lifecycle management, and data drift monitoring/correction.
  • Experience working in a highly regulated environment, with a bonus for Financial or Credit Risk industry exposure.
  • Exceptional communication, presentation, and stakeholder management skills—ability to influence across diverse teams.
  • Master’s degree in Data Science, Computer Science, or a related quantitative discipline.

Subscribe to Future Tech Insights for the latest jobs & insights, direct to your inbox.

By subscribing, you agree to our privacy policy and terms of service.

Industry Insights

Discover insightful articles, industry insights, expert tips, and curated resources.

Careers

How Many Data Science Tools Do You Need to Know to Get a Data Science Job?

If you’re trying to break into data science — or progress your career — it can feel like you are drowning in names: Python, R, TensorFlow, PyTorch, SQL, Spark, AWS, Scikit-learn, Jupyter, Tableau, Power BI…the list just keeps going. With every job advert listing a different combination of tools, many applicants fall into a trap: they try to learn everything. The result? Long tool lists that sound impressive — but little depth to back them up. Here’s the straight-talk version most hiring managers won’t explicitly tell you: 👉 You don’t need to know every data science tool to get hired. 👉 You need to know the right ones — deeply — and know how to use them to solve real problems. Tools matter, but only in service of outcomes. So how many data science tools do you actually need to know to get a job? For most job seekers, the answer is not “27” — it’s more like 8–12, thoughtfully chosen and well understood. This guide explains what employers really value, which tools are core, which are role-specific, and how to focus your toolbox so your CV and interviews shine.

Jobs

What Hiring Managers Look for First in Data Science Job Applications (UK Guide)

If you’re applying for data science roles in the UK, it’s crucial to understand what hiring managers focus on before they dive into your full CV. In competitive markets, recruiters and hiring managers often make their first decisions in the first 10–20 seconds of scanning an application — and in data science, there are specific signals they look for first. Data science isn’t just about coding or statistics — it’s about producing insights, shipping models, collaborating with teams, and solving real business problems. This guide helps you understand exactly what hiring managers look for first in data science applications — and how to structure your CV, portfolio and cover letter so you leap to the top of the shortlist.

Education

The Skills Gap in Data Science Jobs: What Universities Aren’t Teaching

Data science has become one of the most visible and sought-after careers in the UK technology market. From financial services and retail to healthcare, media, government and sport, organisations increasingly rely on data scientists to extract insight, guide decisions and build predictive models. Universities have responded quickly. Degrees in data science, analytics and artificial intelligence have expanded rapidly, and many computer science courses now include data-focused pathways. And yet, despite the volume of graduates entering the market, employers across the UK consistently report the same problem: Many data science candidates are not job-ready. Vacancies remain open. Hiring processes drag on. Candidates with impressive academic backgrounds fail interviews or struggle once hired. The issue is not intelligence or effort. It is a persistent skills gap between university education and real-world data science roles. This article explores that gap in depth: what universities teach well, what they often miss, why the gap exists, what employers actually want, and how jobseekers can bridge the divide to build successful careers in data science.

🍪 We use cookies to enhance your browsing experience on our website. By continuing to use this site, you consent to the use of cookies as described in our Cookie Policy. You can review our Cookie Policy for more information.