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- Tasks: Drive predictive analytics solutions and develop advanced models for real-world challenges.
- Company: Join a forward-thinking company at the forefront of data science and machine learning.
- Benefits: Competitive salary, flexible working options, and opportunities for professional growth.
- Why this job: Make a tangible impact by solving complex problems with cutting-edge technology.
- Qualifications: Proven experience in data science, strong Python skills, and familiarity with Databricks.
- Other info: Collaborate with diverse teams in a dynamic environment focused on innovation.
The predicted salary is between 60000 - 80000 £ per year.
We are seeking a versatile Data Scientist / Machine Learning Engineer to drive high-impact predictive analytics solutions. You will focus on diverse operational challenges, ranging from predictive asset maintenance to dynamic workforce optimisation and demand forecasting. You will leverage the Databricks platform on AWS to build, scale, and deploy robust ML models that integrate seamlessly with our clients' enterprise architectures (SAP, FSM, GIS, SCADA systems).
Key Responsibilities
- End-to-End Predictive Modelling: Design and develop advanced predictive models to solve complex business problems, such as forecasting daily/hourly reactive workloads, predicting asset failures, and optimising resource allocation.
- Databricks Ecosystem Mastery: Utilise Databricks (Unity Catalog, Delta Lake, MLflow) to ingest, process, and analyse large-scale structured and unstructured data from diverse sources (e.g., SAP Datasphere, S3).
- Algorithm Versatility: Apply a wide range of ML techniques, including time-series forecasting (e.g., Prophet, XGBoost, LSTMs), statistical modelling, Bayesian Modelling and optimization algorithms (e.g., Operations Research, Linear Programming) based on the specific use case.
- Scenario Simulation: Build models that allow business users to simulate various operational scenarios (e.g., tweaking risk appetites, reallocating shifts) and evaluate projected outcomes.
- Cross-Functional Collaboration: Work alongside Data Engineers, Gen AI Experts (AWS Bedrock), and UI Developers to build "Compound AI" systems that combine predictive insights with generative AI explanations and user-friendly interfaces.
Required Skills & Qualifications
- Experience: Proven track record as a Data Scientist/ML Engineer delivering predictive models into production environments, ideally for operational, supply chain, or critical infrastructure use cases.
- Programming: Expert-level Python programming (pandas, scikit-learn, statsmodels, PyTorch/TensorFlow).
- Platform Expertise: Deep, hands-on experience with Databricks and the AWS cloud ecosystem.
- Mathematical Foundation: Strong understanding of probability, time-series analysis, and constrained optimization problems.
- Problem Solving: Ability to translate ambiguous business into structured mathematical frameworks.
- Experience in Energy & Utilities industry is a definite advantage.
Data Scientist / Machine Learning Engineer (Predictive Analytics) in Leicester employer: HCLTech
Contact Detail:
HCLTech Recruiting Team
StudySmarter Expert Advice 🤫
We think this is how you could land Data Scientist / Machine Learning Engineer (Predictive Analytics) in Leicester
✨Tip Number 1
Network like a pro! Reach out to folks in the industry on LinkedIn or at meetups. A friendly chat can open doors that a CV just can't.
✨Tip Number 2
Show off your skills! Create a portfolio showcasing your predictive models and projects. Share it during interviews to demonstrate your expertise and passion.
✨Tip Number 3
Prepare for technical interviews by brushing up on your Python and ML algorithms. Practice coding challenges and be ready to discuss your thought process.
✨Tip Number 4
Apply through our website! It’s the best way to ensure your application gets noticed. Plus, we love seeing candidates who are proactive about their job search.
We think you need these skills to ace Data Scientist / Machine Learning Engineer (Predictive Analytics) in Leicester
Some tips for your application 🫡
Tailor Your CV: Make sure your CV is tailored to the Data Scientist / Machine Learning Engineer role. Highlight your experience with predictive modelling and any relevant projects you've worked on, especially those involving Databricks and AWS.
Showcase Your Skills: Don’t just list your skills; demonstrate them! Use specific examples of how you've applied Python, ML techniques, or worked with large datasets in previous roles. This will help us see your expertise in action.
Craft a Compelling Cover Letter: Your cover letter is your chance to shine! Explain why you're passionate about predictive analytics and how your background makes you a perfect fit for our team. Keep it engaging and personal.
Apply Through Our Website: We encourage you to apply through our website for a smoother application process. It helps us keep track of your application and ensures you don’t miss out on any important updates!
How to prepare for a job interview at HCLTech
✨Know Your Predictive Modelling
Make sure you brush up on your predictive modelling skills. Be ready to discuss specific models you've built in the past, especially those related to forecasting and resource optimisation. Prepare examples that showcase your problem-solving abilities and how you tackled complex business challenges.
✨Master the Databricks Ecosystem
Familiarise yourself with the Databricks platform, particularly Unity Catalog, Delta Lake, and MLflow. During the interview, be prepared to explain how you've used these tools to process and analyse large datasets. Highlight any projects where you successfully integrated data from various sources like SAP or S3.
✨Showcase Your Algorithm Versatility
Be ready to discuss a range of machine learning techniques you've applied, such as time-series forecasting or Bayesian modelling. Think of scenarios where you had to choose the right algorithm for a specific use case and be prepared to explain your reasoning behind those choices.
✨Emphasise Cross-Functional Collaboration
Collaboration is key in this role, so think of examples where you've worked with Data Engineers or UI Developers. Share how you contributed to building 'Compound AI' systems and how you ensured that predictive insights were user-friendly and actionable for business users.