DATA SCIENTIST

Reply, Inc.
City of London
1 month ago
Create job alert
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

Related Jobs

View all jobs

Data Scientist

Data Scientist

Data Scientist

Data Scientist

Data Scientist (Government)

Data Scientist - Renewable Energy

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 Data Science Tools Do You Need to Know to Get a Data Science Job?

If you’re trying to break into data science — or progress your career — it can feel like you are drowning in names: Python, R, TensorFlow, PyTorch, SQL, Spark, AWS, Scikit-learn, Jupyter, Tableau, Power BI…the list just keeps going. With every job advert listing a different combination of tools, many applicants fall into a trap: they try to learn everything. The result? Long tool lists that sound impressive — but little depth to back them up. Here’s the straight-talk version most hiring managers won’t explicitly tell you: 👉 You don’t need to know every data science tool to get hired. 👉 You need to know the right ones — deeply — and know how to use them to solve real problems. Tools matter, but only in service of outcomes. So how many data science tools do you actually need to know to get a job? For most job seekers, the answer is not “27” — it’s more like 8–12, thoughtfully chosen and well understood. This guide explains what employers really value, which tools are core, which are role-specific, and how to focus your toolbox so your CV and interviews shine.

What Hiring Managers Look for First in Data Science Job Applications (UK Guide)

If you’re applying for data science roles in the UK, it’s crucial to understand what hiring managers focus on before they dive into your full CV. In competitive markets, recruiters and hiring managers often make their first decisions in the first 10–20 seconds of scanning an application — and in data science, there are specific signals they look for first. Data science isn’t just about coding or statistics — it’s about producing insights, shipping models, collaborating with teams, and solving real business problems. This guide helps you understand exactly what hiring managers look for first in data science applications — and how to structure your CV, portfolio and cover letter so you leap to the top of the shortlist.

The Skills Gap in Data Science Jobs: What Universities Aren’t Teaching

Data science has become one of the most visible and sought-after careers in the UK technology market. From financial services and retail to healthcare, media, government and sport, organisations increasingly rely on data scientists to extract insight, guide decisions and build predictive models. Universities have responded quickly. Degrees in data science, analytics and artificial intelligence have expanded rapidly, and many computer science courses now include data-focused pathways. And yet, despite the volume of graduates entering the market, employers across the UK consistently report the same problem: Many data science candidates are not job-ready. Vacancies remain open. Hiring processes drag on. Candidates with impressive academic backgrounds fail interviews or struggle once hired. The issue is not intelligence or effort. It is a persistent skills gap between university education and real-world data science roles. This article explores that gap in depth: what universities teach well, what they often miss, why the gap exists, what employers actually want, and how jobseekers can bridge the divide to build successful careers in data science.