Senior Data Scientist

Omnis Partners
Sheffield
6 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

๐Ÿš€ Lead Agentic AI Consultant ๐Ÿš€

** Emirati Arabic language skills are ESSENTIAL for this role **



๐Ÿ“ Dubai, UAE

๐Ÿ’ธ 400k - 600k AED + bonus + benefits



We are seeking an experienced Agentic AI Consultant to design, build, and scale autonomous multi-agent systems that move beyond PoCs and into real-world production. This is not โ€œjust another chatbot role", youโ€™ll architect frameworks, deploy systems at scale, and guide clients through the complexities of next-generation AI adoption.



The ideal candidate will unite software engineering depth with data science expertise - capable of hands-on system development while also engaging clients in workshops and strategic roadmaps.



What Youโ€™ll Do

  • Architect multi-agent frameworks (e.g. ReAct, CoT loops, LangGraph) and orchestrate tool-rich workflows.
  • Productionise AI solutions: move beyond PoC into systems serving 100sโ€“1000s of users with robust monitoring and orchestration.
  • Deploy and scale agentic AI across diverse environments (SQL, pandas, RAG, MCP).
  • Evaluate systems rigorously: Pass@N, multi-run testing, retriever validation, and component-level analysis.
  • Educate & consult: Run ideation sessions, maturity assessments, and roadmap planning for enterprise/government clients.
  • Collaborate cross-functionally with AI engineers, architects, and product teams to deliver market-ready solutions.
  • Stay ahead of the curve by exploring LLM advancements, orchestration tools, and evaluation methodologies.



What Weโ€™re Looking For

Required:

  • PhD in AI, Computer Science, or related discipline.
  • 3+ years in AI, Data Science, or advanced technical consulting.
  • Proven track record of building and deploying agentic systems into production.
  • Strong software engineering foundations (deployment, memory orchestration, monitoring).
  • Familiarity with agentic architectures (LangGraph, ReAct, CoT loops).
  • Experience delivering AI at scale โ€“ beyond PoC, into live environments.
  • Ability to engage clients with clear communication and advisory skills.
  • Emirati Arabic language skills are ESSENTIAL for this role



Preferred:

  • Knowledge of TensorFlow, PyTorch, vector databases, retrieval-augmented generation (RAG).
  • Background in start-ups (T-shaped generalists) or consultancies (client-facing exposure).
  • Experience in regulated industries or public sector projects.
  • Self-starter mindset โ€” independent, solutions-driven, and comfortable without hand-holding.



Why Join?

  • Impact at scale: Deliver production-ready systems for Fortune 100s and government bodies.
  • Cutting-edge team: Work alongside industry leaders, academics, and innovators.
  • Global flexibility: Remote-first, with travel and relocation options worldwide.
  • Growth & research: Influence academic research while shaping enterprise AI adoption.
  • Culture: A fast-moving, entrepreneurial team environment with the backing of a global consulting leader.


Keywords (SEO-optimised)

Agentic AI, Multi-Agent Systems, LLM Deployment, LangGraph, LangChain, RAG, AI Orchestration, AI Consultant, Software Engineering for AI, AI Productionisation, Autonomous AI Systems, AI Architect, AI Careers UK, AI Jobs Remote.

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 youโ€™re trying to break into data science โ€” or progress your career โ€” it can feel like you are drowning in names: Python, R, TensorFlow, PyTorch, SQL, Spark, AWS, Scikit-learn, Jupyter, Tableau, Power BIโ€ฆthe 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 โ€” but little depth to back them up. Hereโ€™s the straight-talk version most hiring managers wonโ€™t explicitly tell you: ๐Ÿ‘‰ You donโ€™t need to know every data science tool to get hired. ๐Ÿ‘‰ You need to know the right ones โ€” deeply โ€” 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 โ€œ27โ€ โ€” itโ€™s more like 8โ€“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 youโ€™re applying for data science roles in the UK, itโ€™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 10โ€“20 seconds of scanning an application โ€” and in data science, there are specific signals they look for first. Data science isnโ€™t just about coding or statistics โ€” itโ€™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 โ€” 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 Arenโ€™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.