Senior Data Consultant

Omnis Partners
London
1 month ago
Applications closed

Related Jobs

View all jobs

Senior Data Engineer - Databricks

Senior Data Science Consultant, Customer Data & Technology

Senior Data Science Consultant - Credit Decisioning

Senior Data Science Consultant – Econometrics specialist

Senior Consultant - Data Science

Consultant/Senior Consultant - Data Science Customer Data & Technology

Senior Data Consultant

Disruptive Global Strategy Consultancy



Hybrid / London

£65k - £75k plus bonus & equity



Omnis Partners is delighted to be partnered with a disruptive, innovative strategy consultancy, hiring for highly commercial and strategic Senior Data Consultants with an array of expertise across the data lifecycle and a digital first mindset. You will spearhead the design, shaping and delivery of innovative data science, AI and analytics solutions for a portfolio of clients. This individual will play a pivotal role in seamlessly aligning technical solutions with business objectives, ensuring clients realise maximum value from a range of data products and solutions.



Key Responsibilities:

Solution Design & Delivery

  • Collaborate with clients to understand their business challenges, objectives, and data needs.
  • Design end-to-end data solutions in partnership with engineering and consulting teams, from discovery to deployment, tailored to client goals.
  • Oversee technical delivery teams in delivering solutions that deliver maximum commercial impact and value return for the client.


Client Engagement & Strategy

  • Act as the primary point of contact for clients during project lifecycles, ensuring clarity and alignment on deliverables from a practical and commercial perspective.
  • Provide thought leadership and guidance to clients on data-driven strategies and emerging trends.
  • Translate technical concepts into commercially orientated, business-focused insights and solutions, facilitating informed decision-making for stakeholders.


Collaboration

  • Collaborate with multidisciplinary teams, including Data Architects, Developers, Data Scientists, Engineers, and Analysts, ensuring alignment with project timelines and client objectives.
  • Partner with sales and business development teams to identify opportunities for upselling and expanding client engagements.


Technical Knowledge

  • Oversee the design and implementation of scalable data pipelines, machine learning models, AI products and advanced analytics platforms.
  • Stay abreast of industry trends, tools, and technologies to ensure solutions remain cutting-edge and competitive.
  • Ensure adherence to best practices in data , ML, AI and engineering.



Experience Required:

  • Educated to degree level in Data Science, Machine Learning, Physics, Chemistry, Engineering, Economics, Computer Science, Mathematics, Statistics, or a related field.
  • Proven experience in data science, analytics, AI or engineering fields.
  • Proven track record of successfully delivering complex data projects, with demonstrable problem-solving skills, and a focus on delivering practical, commercially impactful solutions.
  • Strong commercial track record in a formal consulting environment.
  • Ability to wear multiple hats; elements of pre-sales, solution architecture, product management, business analysis and ongoing consultancy to ensure client success
  • Strong understanding of a range of technical programming languages and experience in machine learning frameworks, including architectural design and data platforms.
  • Knowledge of cloud platforms (AWS, Azure, or GCP) and data engineering tools (e.g., Spark, Kafka).
  • Exceptional communication skills, with the ability to influence technical and non-technical stakeholders alike.

Get the latest insights and jobs direct. Sign up for our newsletter.

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

Industry Insights

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

Portfolio Projects That Get You Hired for AI Jobs (With Real GitHub Examples)

In the fast-evolving world of artificial intelligence (AI), an impressive portfolio of projects can act as your passport to landing a sought-after role. Even if you’ve aced interviews in the past, employers in AI and machine learning (ML) are increasingly asking candidates to demonstrate hands-on experience through the projects they’ve built and shared online. This is because practical ability often speaks volumes about your suitability for a role—far more than any exam or certification alone could. In this article, we’ll explore how to build an outstanding AI portfolio that catches the eye of recruiters and hiring managers, including: Why an AI portfolio is crucial for job seekers. How to choose AI projects that align with your target roles. Specific project ideas and real GitHub examples to help you stand out. Best practices for showcasing your work, from writing clear READMEs to using Jupyter notebooks effectively. Tips on structuring your GitHub so that employers can instantly see your value. Moreover, we’ll discuss how you can use your portfolio to connect with top employers in AI, with a handy link to our CV-upload page on Artificial Intelligence Jobs for when you’re ready to apply. By the end, you’ll have a clear roadmap to building a portfolio that will help secure interviews—and the AI job—of your dreams.

AI Job Interview Warm‑Up: 30 Real Coding & System‑Design Questions

In today's competitive AI job market, nailing a technical interview can be the difference between landing your dream role and getting lost in the crowd. Whether you're looking to break into machine learning, deep learning, NLP (Natural Language Processing), or data science, your problem-solving skills and system design expertise are certain to be put to the test. AI‑related job interviews typically involve a range of coding challenges, algorithmic puzzles, and system design questions. You’ll often be asked to delve into the principles of machine learning pipelines, discuss how to optimise large-scale systems, and demonstrate your coding proficiency in languages like Python, C++, or Java. Adequate preparation not only boosts your confidence but also reduces the likelihood of fumbling through unfamiliar territory. If you’re actively seeking positions at major tech companies or innovative AI start-ups, then check out www.artificialintelligencejobs.co.uk for some of the latest vacancies in the UK. Meanwhile, this blog post will guide you through 30 real coding & system-design questions you’re likely to encounter during your AI job interview. This list is designed to help you practise, anticipate typical question patterns, and stay ahead of the competition. By reading through each question and thinking about the possible approaches, you’ll sharpen your problem-solving skills, time management, and critical thinking. Each question covers fundamental concepts that employers regularly test, ensuring you’re well-equipped for success. Let’s dive right in.

Negotiating Your AI Job Offer: Equity, Bonuses & Perks Explained

Artificial intelligence (AI) has proven itself to be one of the most transformative forces in today’s business world. From smart chatbots in customer service to predictive analytics in finance, AI technologies are reshaping how organisations operate and innovate. As the demand for AI professionals grows, so does the complexity of compensation packages. If you’re a mid‑senior AI professional, you’ve likely seen job offers that include far more than just a base salary—think equity, bonuses, and a range of perks designed to entice you into joining or staying with a company. For many, the focus remains squarely on salary. While that’s understandable—after all, your monthly take‑home pay is what covers day-to-day expenses—limiting your negotiations to salary alone can leave considerable value on the table. From stock options in ambitious startups to sign‑on bonuses that ‘buy you out’ of your current contract, modern AI job offers often include elements that can significantly boost your long-term wealth and job satisfaction. This article aims to shed light on the full scope of AI compensation—specifically focusing on how equity, bonuses, and perks can enhance (or sometimes detract from) the overall value of your package. We’ll delve into how these elements work in practice, what to watch out for, and how to navigate the negotiation process effectively. Our goal is to provide mid‑senior AI professionals with the insights and tools to land a holistic compensation deal that accurately reflects their technical expertise, leadership potential, and strategic importance in this fast-moving field. Whether you’re eyeing a leadership role in machine learning at an established tech giant, or you’re considering a pioneering position at a disruptive AI startup, the knowledge in this guide will help you weigh the merits of base salary alongside the potential riches—and risks—of equity, bonuses, and other benefits. By the end, you’ll have a clearer sense of how to align your compensation with both your immediate lifestyle needs and long-term career aspirations.