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Data Scientist

Sage
Newcastle upon Tyne
1 month ago
Applications closed

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Data Scientist

Data Scientist

Data Scientist

Data Scientist

Data Scientist

Data Scientist

Sage Newcastle Upon Tyne, England, United Kingdom

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Job Description

Exciting Job Opportunity: Data Scientist

Job Purpose: Join our dynamic IT team on a mission to revolutionise data delivery worldwide! We emphasize simplicity, mobility, and efficiency, with data and analytics at the heart of enhancing customer experiences and optimizing business processes through innovative solutions.

*This role is a hybrid role – 3 days per week in our Newcastle Office*

Role Overview: As a Data Scientist, reporting to the BI and Analytics Manager, you'll be a pivotal member of our BI and Analytics Hub. You'll develop advanced analytics and machine learning models to transform our understanding and prediction of customer behaviour. Using cutting-edge methodologies and big data technologies, you'll bridge business needs and technical solutions, fostering close collaboration across the organization. Your work will ensure our data-driven solutions are robust, scalable, and impactful.

Key Contributions:

  • Deliver data solutions and services that optimize customer connections across channels.
  • Transform our complex IT data estate by unifying disparate data sources into a single, managed version of the truth.
  • Ensure data integrity through central data mastering and modelling, enabling colleagues to interact with data to meet their needs.
  • Simplify data integrations between systems via a central platform, enhancing user experience and minimizing risk.
  • Promote a culture of data-driven experimentation, showcasing the value of our data through insights and analytics, and demonstrating emerging tech tools.





Key Responsibilities:

  • Develop and own data science solutions, applying statistical/machine-learning models for segmentation, classification, optimisation, and time series analysis.
  • Present findings to the wider team and organisation.
  • Identify insights and suggest recommendations to influence business direction.
  • Develop and optimise churn prediction models to understand customer retention patterns and implement mitigation strategies.
  • Build forecasting models to predict business KPIs, customer lifetime value, and revenue trends using machine learning and statistical techniques.
  • Integrate Large Language Models (LLMs) into RAG-based systems to improve knowledge retrieval and decision support for enterprise applications.
  • Collaborate with data engineers to design scalable data pipelines for machine learning model deployment and inference at scale.
  • Work with cross-functional teams to translate business problems into data science solutions.
  • Develop ETL processes and data transformation workflows for structured and unstructured data.
  • Utilise big data technologies like Spark and Snowflake to process, store, and analyse large datasets efficiently.
  • Optimise and fine-tune LLMs to improve their performance within RAG systems and ensure alignment with business goals.
  • Perform A/B testing and statistical analyses to validate model effectiveness and recommend improvements.
  • Communicate findings and insights to stakeholders through compelling data visualizations and presentations.





Skills, Know-How, and Experience:

  • Strong proficiency in Python (Pandas, NumPy, Scikit-learn, PyTorch/TensorFlow) and SQL.
  • Experience with big data frameworks such as Apache Spark, Databricks, or Dask.
  • Hands-on experience with cloud platforms like AWS (S3, Lambda, SageMaker, Redshift), Azure, or GCP.
  • Knowledge of Snowflake, including Snowpark for scalable data processing and ML integration.
  • Familiarity with MLOps principles, CI/CD pipelines, and model deployment in production environments.
  • Knowledge of NLP techniques and experience with transformer-based LLMs (e.g., OpenAI, Llama, Claude).
  • Strong understanding of machine learning algorithms for classification, regression, clustering, and time series forecasting.
  • Experience with data visualisation tools such as Tableau, Power BI, or Python-based libraries (Matplotlib, Seaborn, Plotly).
  • Excellent problem-solving skills, analytical thinking, and ability to communicate complex technical concepts to non-technical stakeholders.
  • Experience in customer analytics, digital marketing, or e-commerce industries.
  • Familiarity with vector databases and embedding-based retrieval techniques for RAG implementations.
  • Familiarity with modern agentic AI techniques eg Model Context Protocol (MCP)





Technical/Professional Qualifications:

  • Degree in a quantitative discipline (applied mathematics, statistics, computer science, operations research, or related field).
  • Demonstrable experience in exploratory data analysis and feature engineering.
  • Experience with Python, Scikit-learn, PyTorch. Ideally, experience with PySpark, Snowflake, AWS, and GitHub (MLOps practices).





Ready to make a difference with your data science expertise? Apply now and be part of our innovative journey!Seniority level

  • Seniority levelEntry level

Employment type

  • Employment typeFull-time

Job function

  • Job functionEngineering and Information Technology
  • IndustriesSoftware Development

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