DATA SCIENTIST

Reply, Inc.
City of London
2 months ago
Applications closed

Related Jobs

View all jobs

Data Scientist

Data Scientist

Data Scientist

Data Scientist

Data Scientist

Data Scientist

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.

How Many Machine Learning Tools Do You Need to Know to Get a Machine Learning Job?

Machine learning is one of the most exciting and rapidly growing areas of tech. But for job seekers it can also feel like a maze of tools, frameworks and platforms. One job advert wants TensorFlow and Keras. Another mentions PyTorch, scikit-learn and Spark. A third lists Mlflow, Docker, Kubernetes and more. With so many names out there, it’s easy to fall into the trap of thinking you must learn everything just to be competitive. Here’s the honest truth most machine learning hiring managers won’t say out loud: 👉 They don’t hire you because you know every tool. They hire you because you can solve real problems with the tools you know. Tools are important — no doubt — but context, judgement and outcomes matter far more. So how many machine learning tools do you actually need to know to get a job? For most job seekers, the real number is far smaller than you think — and more logically grouped. This guide breaks down exactly what employers expect, which tools are core, which are role-specific, and how to structure your learning for real career results.

What Hiring Managers Look for First in Machine Learning Job Applications (UK Guide)

Whether you’re applying for machine learning engineer, applied scientist, research scientist, ML Ops or data scientist roles, hiring managers scan applications quickly — often making decisions before they’ve read beyond the top third of your CV. In the competitive UK market, it’s not enough to list skills. You must send clear signals of relevance, delivery, impact, reasoning and readiness for production — and do it within the first few lines of your CV or portfolio. This guide walks you through exactly what hiring managers look for first in machine learning applications, how they evaluate CVs and portfolios, and what you can do to improve your chances of getting shortlisted at every stage — from your CV and LinkedIn profile to your cover letter and project portfolio.

MLOps Jobs in the UK: The Complete Career Guide for Machine Learning Professionals

Machine learning has moved from experimentation to production at scale. As a result, MLOps jobs have become some of the most in-demand and best-paid roles in the UK tech market. For job seekers with experience in machine learning, data science, software engineering or cloud infrastructure, MLOps represents a powerful career pivot or progression. This guide is designed to help you understand what MLOps roles involve, which skills employers are hiring for, how to transition into MLOps, salary expectations in the UK, and how to land your next role using specialist platforms like MachineLearningJobs.co.uk.