DATA SCIENTIST

Reply, Inc.
City of London
1 month ago
Applications closed

Related Jobs

View all jobs

Data Scientist

Data Scientist

Data Scientist

Data Scientist

Data Scientist - Measurement Specialist

Data Scientist - Imaging - Remote - Outside IR35

Career Opportunities: Data Scientist (10888)

Requisition ID10888-Posted - Years of Experience (1) -Technology- Where (1) -Job


Data Reply is the Reply Group company offering a broad range of analytics and data processing services. We operate across different industries and business functions, working directly with executive level professionals, enabling them to achieve meaningful outcomes through effective use of data. We find that one of the biggest problems experienced by our clients today is being overwhelmed with the amount of data that they face and not knowing how to leverage it to their advantage. The vast landscape of available technology stacks and models means that choosing the right ones can be a daunting task. Most companies know that their data is valuable, and that they should be making the most out of it to stay competitive, but often don’t know where to begin or what to prioritise. At Data Reply, we pride ourselves on helping clients make the right decisions to build their data strategy. With our consultants’ expertise, we map the right technologies to meet our clients’ business needs. We deal in bespoke solutions, and offer in house training to ensure that our clients realise the full value of their big data solution.


Role Overview

As a Data Scientist at Data Reply, you will play a hands‑on role in designing, building, and deploying data‑driven solutions using machine learning (ML) and generative AI (GenAI) techniques on AWS. You will work alongside senior data scientists and engineers to transform business problems into scalable ML solutions and contribute to end‑to‑end project delivery in an enterprise setting.


This role is ideal for someone with 1–2 years of professional experience in data science who has worked on at least 2–3 enterprise‑level projects and is eager to deepen their expertise in modern ML frameworks, cloud technologies, and emerging AI domains such as computer vision or GenAI.


Responsibilities

  • Develop, train, and evaluate machine learning models using Python and popular frameworks (scikit‑learn, TensorFlow, PyTorch)
  • Conduct exploratory data analysis, feature engineering, model optimization, and apply statistical modeling techniques
  • Build and deploy ML models on AWS SageMaker, collaborating with MLOps engineers to integrate solutions using AWS services
  • Ensure responsible AI by implementing model explainability and bias detection techniques
  • Apply deep learning models (e.g., RNN, LSTM) on client projects and prototype new AI capabilities (multi‑modal, synthetic data, agent‑based systems)
  • Work with cross‑functional teams to deliver scalable AI solutions, and translate technical results into client recommendations
  • Document methodologies, maintain reproducibility, share knowledge internally, and stay updated on trends in data science and cloud ML

About the Candidate

  • 1–2 years of hands‑on experience in data science or applied machine learning in an enterprise setting
  • Strong understanding of AWS services, particularly SageMaker, S3, and Bedrock
  • Proficiency in Python with experience using NumPy, pandas, scikit‑learn, and one deep learning framework (PyTorch or TensorFlow)
  • Experience working with structured and unstructured data, using SQL or Pandas for data manipulation
  • Experience using Git, Jupyter Notebooks, and collaborative environments
  • Experience in computer vision, natural language processing (NLP), or generative AI applications
  • Familiarity with LangChain, Hugging Face, or OpenAI APIs for working with LLMs
  • Experience with data pipeline tools (e.g., Airflow, Step Functions) or data validation frameworks (e.g., Great Expectations)

Reply is an Equal Opportunities Employer and committed to embracing diversity in the workplace. We provide equal employment opportunities to all employees and applicants for employment and prohibit discrimination and harassment of any type regardless of age, sexual orientation, gender, identity, pregnancy, religion, nationality, ethnic origin, disability, medical history, skin colour, marital status or parental status or any other characteristic protected by the Law.


Reply is committed to making sure that our selection methods are fair to everyone. To help you during the recruitment process, please let us know of any Reasonable Adjustments you may need.


#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.

The Skills Gap in Machine Learning Jobs: What Universities Aren’t Teaching

Machine learning has moved from academic research into the core of modern business. From recommendation engines and fraud detection to medical imaging, autonomous systems and language models, machine learning now underpins many of the UK’s most critical technologies. Universities have responded quickly. Machine learning modules are now standard in computer science degrees, specialist MSc programmes have proliferated, and online courses promise to fast-track careers in the field. And yet, despite this growth in education, UK employers consistently report the same problem: Many candidates with machine learning qualifications are not job-ready. Roles remain open for months. Interview processes filter out large numbers of applicants. Graduates with strong theoretical knowledge struggle when faced with practical tasks. The issue is not intelligence or effort. It is a persistent skills gap between university-level machine learning education and real-world machine learning jobs. This article explores that gap in depth: what universities teach well, what they routinely miss, why the gap exists, what employers actually want, and how jobseekers can bridge the divide to build successful careers in machine learning.

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

Are you considering a career change into machine learning in your 30s, 40s or 50s? You’re not alone. In the UK, organisations across industries such as finance, healthcare, retail, government & technology are investing in machine learning to improve decisions, automate processes & unlock new insights. But with all the hype, it can be hard to tell which roles are real job opportunities and which are just buzzwords. This article gives you a practical, UK-focused reality check: which machine learning roles truly exist, what skills employers really hire for, how long retraining realistically takes, how to position your experience and whether age matters in your favour or not. Whether you come from analytics, engineering, operations, research, compliance or business strategy, there is a credible route into machine learning if you approach it strategically.

How to Write a Machine Learning Job Ad That Attracts the Right People

Machine learning now sits at the heart of many UK organisations, powering everything from recommendation engines and fraud detection to forecasting, automation and decision support. As adoption grows, so does demand for skilled machine learning professionals. Yet many employers struggle to attract the right candidates. Machine learning job adverts often generate high volumes of applications, but few applicants have the blend of modelling skill, engineering awareness and real-world experience the role actually requires. Meanwhile, strong machine learning engineers and scientists quietly avoid adverts that feel vague, inflated or confused. In most cases, the issue is not the talent market — it is the job advert itself. Machine learning professionals are analytical, technically rigorous and highly selective. A poorly written job ad signals unclear expectations and low ML maturity. A well-written one signals credibility, focus and a serious approach to applied machine learning. This guide explains how to write a machine learning job ad that attracts the right people, improves applicant quality and strengthens your employer brand.