Data Scientist

One Market Data LLC
Belfast
1 day ago
Create job alert
About the Role

We are building advanced analytics and machine learning solutions on top of our high-performance FinTech platform. As part of the LLM & ML Analytics Team, you’ll automate, design, implement, and deploy monitoring, reporting, and forecasting systems - delivering actionable insights and enabling advanced workflows, including natural language analytics powered by LLM agents.

Prior to advancing with your application, we kindly request that you review the CONSENT NOTICE FOR HR AND RECRUITING provided by OneMarketData. Your attention to this matter is greatly appreciated.

What You’ll Work On
  • Develop statistical models and machine learning algorithms (e.g., anomaly detection, clustering, regression) with an understanding of customer demands.
  • Prepare, engineer features, and analyze large-scale financial datasets.
  • Build and maintain production-ready ML pipelines for model training, validation, and performance monitoring.
  • Design and implement workflows for reporting, alerting, and forecasting.
  • Develop and refine LLM-powered agents to enable natural language interaction and analytics automation.
  • Drive the deployment and support of your products.
What We’re Looking For
  • 2-4 years of experience in Data Science, including hands-on development and validation of statistical models and ML solutions.
  • Strong proficiency in Python and data analysis libraries (numpy, pandas, scikit-learn, LightGBM).
  • Solid understanding of Object-Oriented Programming (OOP) principles and mandatory experience writing unit tests.
  • Solid knowledge of database systems and ETL processes (SQL, data aggregation).
  • Practical experience integrating LLM APIs and related tools (OpenAI API, MCP, Langfuse).
  • Strong engineering focus on product integration and deployment.
  • Proficient spoken, written, and reading English skills required.
Highly Desirable
  • Understanding of financial market data and experience working with FinTech platforms.
  • Experience working with time series data and forecasting models.
  • Strong foundation in statistics and probability theory.
  • Exposure to advanced LLM techniques and frameworks (e.g., RAG, LangGraph, multi-agent pipelines).
  • Experience with MLOps practices, containerization, CI/CD pipelines, and model monitoring in production.
Why Join Us?
  • Join a high-caliber team at the intersection of AI and FinTech.
  • Drive the development of business-critical features and share your expertise through technical articles and demos. For example, here is our medium space where we publish our achievements.
  • Work with a cutting-edge stack: LightGBM, Langchain, Langfuse, AWS and more.
  • Influence the direction of our products and deliver rapid, tangible impact.
  • Enjoy a collaborative environment with fast release cycles and a strong engineering culture.
Work Location & Hybrid Model

This role follows a hybrid work model and requires regular in-office collaboration. Candidates should be based within commuting distance of one of our offices:

Belfast – The Weaving Works, Ormeau Avenue, Northern Ireland

Dublin – 1 George’s Quay Plaza, Dublin 2, Republic of Ireland

Equal Employment Opportunity

Equal Employment Opportunity (EEO) Employer, OneMarketData prohibits discriminatory employment actions against and treatment of its employees and applicants for employment based on actual or perceived race or color, size (including bone structure, body size, height, shape, and weight), religion or creed, alienage or citizenship status, sex (including pregnancy), national origin, age, sexual orientation, gender identity (one’s internal deeply-held sense of one’s gender which may be the same or different from one’s sex assigned at birth); gender expression (the representation of gender as expressed through, for example, one’s name, choice of pronouns, clothing, haircut, behavior, voice, or body characteristics; gender expression may not conform to traditional gender-based stereotypes assigned to specific gender identities), disability, marital status, relationship and family structure (including domestic partnerships, polyamorous families and individuals, chosen family, platonic co-parents, and multigenerational families), genetic information or predisposing genetic characteristics, military status, domestic violence victim status, arrest or pre-employment conviction record, credit history, unemployment status, caregiver status, salary history, or any other characteristic protected by law.

The position will require a background check, signed NDA, signed contract, and signed GDPR processor passthrough agreement (since we act as a data processor under GDPR). Salaries will be commensurate with experience, education, skillset, and local norms. Kindly note that only shortlisted candidates will be contacted for an interview.


#J-18808-Ljbffr

Related Jobs

View all jobs

Data Scientist

Data Scientist

Data Scientist

Data Scientist

Data Scientist

Data Scientist (Government)

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.

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.

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.

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.