Be at the heart of actionFly remote-controlled drones into enemy territory to gather vital information.

Apply Now

Data Scientist ML Engineer

Experis
Birmingham
3 months ago
Applications closed

Related Jobs

View all jobs

Staff Data Scientist

Junior Data Scientist Data & ML Engineering Focus Remote UK Only

Data Scientist / Quant Engineer

Senior Data Scientist - Payments

Data Scientist

Data Scientist

Social network you want to login/join with:

Location: Birmingham Job Type: Contract Industry: Cloud & Infrastructure Job reference: BBBH420156_1754904824 Posted: about 10 hours ago

Role Title: Data Scientist ML Engineer

Duration: contract to run until 28/11/2025

Location: Birmingham/Sheffield, Hybrid 2-3 days onsite

Rate: TBC

Key Skills/Requirements

  • Carrying out preprocessing of structured and unstructured data
  • Enhancing data collection procedures to include all relevant information for developing analytic systems
  • Processing, cleansing, and validating the integrity of data to be used for analysis
  • Analyzing large amounts of information to find patterns and solutions
  • Developing prediction systems and machine learning algorithms
  • Presenting results in a clear manner
  • Proposing solutions and strategies to tackle business challenges
  • Data mining or extracting usable data from valuable data sources
  • Using machine learning tools to select features, create, and optimize classifiers

Qualifications

  • Programming Skills: knowledge of statistical programming languages like Python, and database query languages like SQL, Hive/Hadoop, Pig is desirable. Familiarity with Scala and Java is an added advantage.
  • Statistics: Good applied statistical skills, including knowledge of statistical tests, distributions, regression, maximum likelihood estimators, etc. Proficiency in statistics is essential for data-driven companies.
  • Machine Learning: good knowledge of machine learning methods like decision-making, k-Nearest Neighbors, Naive Bayes, SVM, Decision Forests.
  • Strong Math Skills (Multivariable Calculus and Linear Algebra): understanding the fundamentals of Multivariable Calculus and Linear Algebra is important as they form the basis of many predictive performance or algorithm optimization techniques.
  • Data Wrangling: proficiency in handling imperfections in data is an important aspect of a data scientist job description.
  • Experience with Data Visualization Tools like Splunk, PowerBi that help to visually encode data

All profiles will be reviewed against the required skills and experience. Due to the high number of applications, we will only be able to respond to successful applicants in the first instance. We thank you for your interest and the time taken to apply!


#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.

Data Science Recruitment Trends 2025 (UK): What Job Seekers Need To Know About Today’s Hiring Process

Summary: UK data science hiring has shifted from title‑led CV screens to capability‑driven assessments that emphasise rigorous problem framing, high‑quality analytics & modelling, experiment/causality, production awareness (MLOps), governance/ethics, and measurable product or commercial impact. This guide explains what’s changed, what to expect in interviews & how to prepare—especially for product/data scientists, applied ML scientists, decision scientists, econometricians, growth/marketing analysts, and ML‑adjacent data scientists supporting LLM/AI products. Who this is for: Product/decision/data scientists, applied ML scientists, econometrics & causal inference specialists, experimentation leads, analytics engineers crossing into DS, ML generalists with strong statistics, and data scientists collaborating with platform/MLOps teams in the UK.

Why Data Science Careers in the UK Are Becoming More Multidisciplinary

Data science once meant advanced statistics, machine learning models and coding in Python or R. In the UK today, it has become one of the most in-demand professions across sectors — from healthcare to finance, retail to government. But as the field matures, employers now expect more than technical modelling skills. Modern data science is multidisciplinary. It requires not just coding and algorithms, but also legal knowledge, ethical reasoning, psychological insight, linguistic clarity and human-centred design. Data scientists are expected to interpret, communicate and apply data responsibly, with awareness of law, human behaviour and accessibility. In this article, we’ll explore why data science careers in the UK are becoming more multidisciplinary, how these five disciplines intersect with data science, and what job-seekers & employers need to know to succeed in this transformed field.

Data Science Team Structures Explained: Who Does What in a Modern Data Science Department

Data science is one of the most in-demand, dynamic, and multidisciplinary areas in the UK tech and business landscape. Organisations from finance, retail, health, government, and beyond are using data to drive decisions, automate processes, personalise services, predict trends, detect fraud, and more. To do that well, companies don’t just need good data scientists; they need teams with clearly defined roles, responsibilities, workflows, collaboration, and governance. If you're aiming for a role in data science or recruiting for one, understanding the structure of a data science department—and who does what—can make all the difference. This article breaks down the key roles, how they interact across the lifecycle of a data science project, what skills and qualifications are typical in the UK, expected salary ranges, challenges, trends, and how to build or grow an effective team.