Gen AI Specialist

London
9 months ago
Applications closed

Related Jobs

View all jobs

Data Engineer

Data Engineer

Data Engineer - Databricks Specialist - NonVolume

Data Engineer - Databricks Specialist - NonVolume

Data Engineer – Databricks Specialist

Data Engineer - Databricks Specialist

Gen AI Specialist
Location: Canary Wharf, London (3 days onsite)
Contract Length: 10 months
Daily Rate: £800 - £850 (inside IR35 via umbrella)

Are you a seasoned Data Scientist with a passion for Generative AI? Our client is seeking a Gen AI Specialist to join their dynamic Technology team in Canary Wharf. This role offers an exciting opportunity to work on innovative solutions that address complex financial data challenges, particularly in credit risk management.

Key Responsibilities:

Lead the development and coordination of analytical plans, ensuring alignment with various teams.
Manage deliverables in an agile environment while maintaining clear and effective communication with stakeholders.
Present analytical findings, updates, and challenges to diverse audiences including business units, technology management, and risk review teams.
Execute data modelling and cleaning processes utilising both internal and external data sources.
Build predictive and prescriptive models through data manipulation and cleaning.
Design, manage, and deploy analytical solutions leveraging Machine Learning (ML), Deep Learning (DL), and Large Language Models (LLMs) into production systems following the technology SDLC process.
Implement features throughout the ML lifecycle-Development, Testing, Training, Production, and Monitoring-to ensure the scalability and reliability of solutions.Qualifications:

PhD or master's degree in Computer Science, Data Science, Statistics, Mathematics, Engineering, or a related field.
Minimum of 5 years of industry experience as a data scientist, with a focus on ML modelling, Ranking, Recommendations, or Personalization systems.
Proven track record of designing and developing scalable and reliable machine learning systems.
Strong expertise in ML/DL/LLM algorithms, model architectures, and training techniques.
Proficiency in programming languages such as Python, SQL, Spark, PySpark, TensorFlow, or equivalent analytical/model-building tools.
Familiarity with tools and technologies related to LLMs.
Ability to work independently while also thriving in a collaborative team environment.
Experience with GenAI/LLMs projects.
Familiarity with distributed data/computing tools (e.g., Hadoop, Hive, Spark, MySQL).
Background in financial services, including banking or risk management.
Knowledge of capital markets and financial instruments, along with modelling expertise.

If you are a forward-thinking individual with an adaptive mindset ready to tackle complex business problems, we want to hear from you! Join our client's innovative team and contribute to the future of financial data analysis.

To Apply: Please submit your CV and a cover letter detailing your relevant experience and interest in the role.

Our client is an equal opportunity employer and welcomes applicants from diverse backgrounds.

Adecco is a disability-confident employer. It is important to us that we run an inclusive and accessible recruitment process to support candidates of all backgrounds and all abilities to apply. Adecco is committed to building a supportive environment for you to explore the next steps in your career. If you require reasonable adjustments at any stage, please let us know and we will be happy to support you

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 Jobs

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.

Jobs

How to Write a Data Science Job Ad That Attracts the Right People

Data science plays a critical role in how organisations across the UK make decisions, build products and gain competitive advantage. From forecasting and personalisation to risk modelling and experimentation, data scientists help translate data into insight and action. Yet many employers struggle to attract the right data science candidates. Job adverts often generate high volumes of applications, but few applicants have the mix of analytical skill, business understanding and communication ability the role actually requires. At the same time, experienced data scientists skip over adverts that feel vague, inflated or misaligned with real data science work. In most cases, the issue is not a lack of talent — it is the quality and clarity of the job advert. Data scientists are analytical, sceptical of hype and highly selective. A poorly written job ad signals unclear expectations and immature data practices. A well-written one signals credibility, focus and serious intent. This guide explains how to write a data science job ad that attracts the right people, improves applicant quality and positions your organisation as a strong data employer.

Education Jobs

Maths for Data Science Jobs: The Only Topics You Actually Need (& How to Learn Them)

If you are applying for data science jobs in the UK, the maths can feel like a moving target. Job descriptions say “strong statistical knowledge” or “solid ML fundamentals” but they rarely tell you which topics you will actually use day to day. Here’s the truth: most UK data science roles do not require advanced pure maths. What they do require is confidence with a tight set of practical topics that come up repeatedly in modelling, experimentation, forecasting, evaluation, stakeholder comms & decision-making. This guide focuses on the only maths most data scientists keep using: Statistics for decision making (confidence intervals, hypothesis tests, power, uncertainty) Probability for real-world data (base rates, noise, sampling, Bayesian intuition) Linear algebra essentials (vectors, matrices, projections, PCA intuition) Calculus & gradients (enough to understand optimisation & backprop) Optimisation & model evaluation (loss functions, cross-validation, metrics, thresholds) You’ll also get a 6-week plan, portfolio projects & a resources section you can follow without getting pulled into unnecessary theory.

🍪 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.