Senior Data Scientist

AryaXAI
London
3 months ago
Applications closed

Related Jobs

View all jobs

Senior Data Scientist

Senior Data Scientist

Senior Data Scientist

Senior Data Scientist

Senior Data Scientist

Senior Data Scientist

Direct message the job poster from AryaXAI

Interpreting and Aligning AI for mission-critical use cases

AryaXAI stands at the forefront of AI innovation, revolutionizing how AI is applied in mission-critical, highly regulated industries by building explainable, safe, and aligned systems that scale responsibly. Our mission is to empower banks, financial institutions, energy companies, and other large enterprises to unlock AI’s full potential—while meeting the highest standards of transparency, compliance, and risk management.

We thrive on deep collaboration, hands-on ownership, and a relentless drive to push the boundaries of what AI can do in real-world, high-stakes scenarios. At AryaXAI, every team member plays a critical role in shaping the future of trustworthy AI systems.

Role Overview

As a Senior Data Scientist, you'll lead the development and deployment of advanced machine learning solutions tailored for regulated, high-impact environments, with a primary focus on highly regulated industries like financial services, banking, insurance etc. Your work will directly support critical use cases such as fraud detection, underwriting, anomaly detection, and other risk-sensitive operations. This is a hands-on, strategic role where explainability, model auditability, and operational integration are just as important as accuracy and innovation.

Responsibilities:

  • Fine-tune, evaluate, and implement classic and deep learning models optimized for enterprise and regulatory environments.
  • Balance accuracy, efficiency, scalability, and compliance across a wide range of business-critical use cases.

Architectural Assessment

  • Assess and recommend appropriate model architectures that align with business goals and satisfy regulatory and infrastructure constraints, especially in FSI environments.
  • Lead seamless integration of AI models into enterprise systems—on-prem or in the cloud—ensuring low-latency performance, traceability, and security.

Specialization in Regulated Use Cases

  • Design AI solutions for complex applications including fraud monitoring, underwriting, risk scoring, AML, anomaly detection, and financial forecasting.
  • Collaborate with domain experts and compliance teams to incorporate regulatory guardrails and auditability directly into the ML lifecycle.

AI Governance & Explainability

  • Leverage and contribute to AryaXAI’s inferencing stack to ensure that models are not just powerful—but trustworthy, interpretable, and aligned.

Cross-Functional Collaboration

  • Work with ML engineers, data platform teams, and product stakeholders to deliver scalable, production-grade solutions that are aligned with enterprise needs.

Documentation & Compliance

  • Maintain rigorous documentation and model governance records suitable for internal audits and external regulatory reviews.

Qualifications

Core Experience

  • 5+ years of experience applying data science or ML in regulated industries (financial services, banking, insurance, energy, etc.).
  • Demonstrated success in building ML models for mission-critical applications where compliance, accuracy, and robustness are key.
  • Hands-on experience in one or more of the following domains is highly valued:
  • → Underwriting, fraud detection, anomaly detection, credit scoring, AML/KYC, operational risk.

Technical Proficiency

  • Strong command of Python and experience with ML/DL frameworks such as scikit-learn, PyTorch, TensorFlow, XGBoost etc.
  • Comfortable working with complex, multimodal datasets including tabular, textual, and time-series data.
  • Familiarity with ML Ops pipelines for deployment, monitoring, and lifecycle management of models in production.
  • Experience with model interpretability and fairness tools (e.g., SHAP, LIME, Captum).

Bonus Points

  • Knowledge of regulatory standards (e.g., model risk management, explainability mandates, bias/fairness compliance).
  • Experience in hybrid or multi-cloud environments, including secure, on-premise model deployment.
  • Contributions to peer-reviewed publications, open-source projects, or internal AI governance initiatives.

What You’ll Get

  • Competitive salary and meaningful equity
  • Premium health insurance for you and your family
  • Opportunity to work on high-impact, real-world AI challenges in finance, insurance, energy, and beyond
  • Direct influence over platform strategy and model design at a high-growth AI company
  • Career progression in a flat, high-performance team that values craft, curiosity, and integrity

If you’re passionate about deploying AI where it truly matters, in sectors where accuracy, auditability, and trust are non-negotiable—we’d love to talk.

Seniority level

  • Seniority levelMid-Senior level

Employment type

  • Employment typeFull-time

Job function

  • Job functionEngineering, Information Technology, and Analyst
  • IndustriesIT Services and IT Consulting and Software Development

Referrals increase your chances of interviewing at AryaXAI by 2x

London, England, United Kingdom 5 days ago

London, England, United Kingdom 2 weeks ago

London, England, United Kingdom 3 weeks ago

London, England, United Kingdom 1 week ago

Associate (Data Scientist) - London, Wroclaw

London, England, United Kingdom 5 hours ago

Greater London, England, United Kingdom 2 months ago

Data Scientist, Internship, United Kingdom - BCG X

London, England, United Kingdom 3 days ago

London, England, United Kingdom 2 weeks ago

London, England, United Kingdom 4 days ago

Data Scientist, Marketing Insights and Analytics

London, England, United Kingdom 3 weeks ago

London, England, United Kingdom 2 days ago

We’re unlocking community knowledge in a new way. Experts add insights directly into each article, started with the help of AI.


#J-18808-Ljbffr

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.

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.

Data Science Jobs for Career Switchers in Their 30s, 40s & 50s (UK Reality Check)

Thinking about switching into data science in your 30s, 40s or 50s? You’re far from alone. Across the UK, businesses are investing in data science talent to turn data into insight, support better decisions and unlock competitive advantage. But with all the hype about machine learning, Python, AI and data unicorns, it can be hard to separate real opportunities from noise. This article gives you a practical, UK-focused reality check on data science careers for mid-life career switchers — what roles really exist, what skills employers really hire for, how long retraining typically takes, what UK recruiters actually look for and how to craft a compelling career pivot story. Whether you come from finance, marketing, operations, research, project management or another field entirely, there are meaningful pathways into data science — and age itself is not the barrier many people fear.