Data Scientist - Reply

Data Scientist

About Data Reply:
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 prohibits 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.