The Senior Data Scientist will design, build, and deploy data science solutions for nxzen's critical national infrastructure clients (primarily energy and utilities such as water, gas, and electricity, expanding in the mid-term to rail and highways sectors) in the UK, Australia, and internationally, applying predictive analytics and statistical modelling, optimisation and simulation to real operational problems, and leveraging machine learning and AI as tools where they add value.
Based in nxzen’s Global Capability Centre in Hyderabad or Bangalore, you will report to the Director of AI and Analytics and work as part of a growing Data Science team delivering production-grade analytical and predictive solutions for critical national infrastructure operators. This is a hands-on technical delivery role with a team leadership and coaching element. You will write production code, build predictive models, run simulations, and work directly with client data, while leading and mentoring small teams of data professionals alongside you.
The role spans the full data science lifecycle: from problem framing and use case discovery, through exploratory data analysis and feature engineering, to model development, validation, and production deployment. Your core discipline is predictive analytics and statistical modelling, optimisation and simulation. You will apply machine learning techniques (regression, classification, clustering, time-series forecasting, deep learning) and generative AI (LLM integration, RAG pipelines, agentic workflows) where they are the right tool for the problem. You will work closely with data engineers, analytics leads, and domain consultants to ensure solutions are robust, explainable, and deliver measurable business outcomes.
You will contribute to nxzen’s IP and solution development, and support pre-sales by building proof-of-value prototypes, technical demonstrations, and contributing to proposals and commercial presentations under the direction of the Director of AI and Analytics and the Head of the Data & AI practice. Success in this role requires strong applied data science skills, comfort with messy real-world data, the ability to communicate technical results to non-technical stakeholders, and a genuine interest in energy and infrastructure as an application domain.
Key responsibilities
The Senior Data Scientist will design and deliver data science solutions across nxzen’s critical national infrastructure client portfolio, combining analytical depth with practical delivery focus. You will work on client engagements from discovery through to production, collaborating with onshore/offshore consultants, data engineers, and client technical teams.
You will own the end-to-end model development lifecycle, from problem structuring and data exploration through to deployment and monitoring, ensuring solutions are production-grade, explainable, and aligned to client requirements and nxzen’s delivery standards.
A key focus will be translating business problems into well-structured analytical and predictive solutions that deliver measurable value for regulated critical national infrastructure clients. You will contribute to the growth and technical maturity of nxzen’s Global Capability Centre data science function.
· Design, develop, and deploy predictive analytics and statistical models for critical national infrastructure use cases including asset risk scoring (CNAIM/NARM), predictive maintenance, leak and outage prediction, demand forecasting, failure probability modelling, and network performance optimisation, applying machine learning techniques where they improve accuracy or scalability.
· Apply rigorous statistical methods (survival analysis, Bayesian inference, regression modelling, ensemble methods) to underpin the predictive models above, working with operational data including SCADA historian, smart meter, weather, and IoT sensor streams.
· Build optimisation and simulation models to support client decision-making, including workforce scheduling, capital investment prioritisation, network capacity planning, and maintenance strategy optimisation. Techniques include linear and mixed-integer programming, Monte Carlo simulation, and agent-based modelling. Experience tailored to energy and utilities contexts is desirable but not essential.
· Perform exploratory data analysis, feature engineering, and data quality assessment on operational, asset, geospatial, SCADA, and financial datasets typical of energy and utilities environments.
· Apply machine learning and deep learning techniques to computer vision problems including asset defect detection, vegetation management, and infrastructure inspection, using image classification, object detection, and segmentation models.
· Design and deploy generative AI solutions where appropriate, including RAG pipelines, agentic workflows, and intelligent assistants for field engineers, regulatory analysts, and operations teams. Generative AI is one tool in the toolkit, not the default answer.
· Build and maintain deployment pipelines (MLOps, LLMOps) for model training, validation, production deployment, monitoring, and retraining, using platforms such as Azure ML, AWS SageMaker, Databricks, or equivalent.
· Contribute to nxzen IP development (e.g., intelligent IT service management and field knowledge assistant platforms), embedding analytical and predictive capabilities where they add value.
· Support pre-sales and business development by building proof-of-value prototypes, technical demonstrations, and solution architecture inputs for client proposals and commercial presentations, working under the direction of the Director of AI and Analytics and the Head of the Data & AI practice.
· Apply responsible AI and model risk management principles to all model development, including explainability, fairness, bias detection, model documentation, and alignment with the UK AI Regulation White Paper, NCSC Guidelines, and ISO/IEC 42001.
· Lead small delivery teams of data professionals on client engagements, including work allocation, technical direction, code review, and supporting team members' career development. Comfortable combining hands-on technical delivery with team leadership responsibilities.
· Mentor more junior team members and support peer learning within the Data & AI practice, contributing to code review and technical standards development.
· Collaborate with onshore delivery leads and client data teams through regular stand-ups, sprint planning, and technical reviews, operating effectively in a distributed onshore/offshore model across geographies and time zones.
· Work with data engineers to ensure model inputs are well-governed, documented, and traceable, supporting end-to-end data lineage from source systems through to model outputs.
· Stay current with advances in data science, statistical methods, optimisation techniques, machine learning, and generative AI (e.g., conformal prediction for distribution-free uncertainty quantification with guaranteed coverage, decision-focused learning for training predictive models against downstream decision quality, and time-series
foundation models for zero-shot forecasting on unseen data), evaluating new approaches for applicability to nxzen's client problems.
· Contribute to nxzen’s technical thought leadership in the data science space through internal knowledge sharing, technical blog posts, and reusable solution accelerators.
What we’re looking for
We are looking for a data scientist whose core strength is predictive analytics, statistical modelling, and quantitative problem-solving, with the ability to apply machine learning and AI techniques as tools where they fit. This is not a research-only role. We need someone who can take a business problem, structure it as an analytical or predictive task, select the right method, build and validate models, and deploy them with proper monitoring. We expect at least 7 years of professional experience in data science, applied quantitative analytics, or related roles, with at least 5 years in hands-on model development and deployment. A Bachelor’s degree in a quantitative discipline (Computer Science, Mathematics, Statistics, Physics, Engineering, or similar) is expected. A Master’s or PhD in a relevant field is desirable.
You will work in a distributed team with onshore/offshore consultants and client stakeholders in the UK and Australia. Strong written and spoken English is essential. You must be comfortable presenting technical findings to non-technical audiences and working across time zones. Experience in energy, utilities, or infrastructure is highly desirable but not essential.
· Strong proficiency in Python for data science and analytical development, including core libraries: pandas, NumPy, SciPy, statsmodels, scikit-learn, XGBoost, LightGBM, and at least one deep learning framework (PyTorch preferred, TensorFlow accepted). Experience with R is a plus.
· At least 5 years applying predictive analytics and statistical modelling techniques to real business problems, including regression techniques, survival analysis, Bayesian methods, hypothesis testing, experimental design, and time-series forecasting. Able to select and justify the right method for the problem, not just the most complex one.
· Experience with optimisation, simulation, and decision modelling techniques (linear/mixed-integer programming, Monte Carlo simulation, agent-based modelling, discrete event simulation) is desirable. Application to energy and utilities problems such as capital planning, outage scheduling, or network capacity is a strong plus.
· At least 2 years leading data or analytics teams (formally or informally) on client engagements or internal projects, including work allocation, technical direction, code review, and supporting team members' development. Comfortable combining hands-on delivery with team leadership responsibilities.
· At least 3 years of hands-on experience building and deploying predictive and analytical models in production, including model training, validation, performance monitoring, and iterative improvement. Not just Jupyter notebooks. We expect experience with production deployment patterns.
· Strong understanding of offline and online evaluation of AI and ML models. Offline: cross-validation, holdout testing, classification and regression metrics (precision, recall, F1,
AUC, RMSE, MAE), calibration, and robustness checks. Online: A/B testing, shadow deployments, champion-challenger evaluation, monitoring for data drift, concept drift, and performance degradation in production. Able to design appropriate evaluation strategies for the problem at hand.
· At least 2 years working with MLOps tooling and practices for productionising models: model versioning (MLflow, Weights & Biases), CI/CD for deployment pipelines, containerisation (Docker), and orchestration (Airflow, Kubeflow, or equivalent).
· Experience with at least one major cloud ML platform: Azure ML, AWS SageMaker, Databricks ML, or Google Vertex AI.
· Experience with generative AI techniques as applied tools, including LLM fine-tuning, prompt engineering, RAG architectures, vector databases, and agentic frameworks (LangChain, crew.ai, Semantic Kernel, or equivalent). Familiarity with OpenAI, Anthropic Claude, Gemini, Llama, and Mistral APIs.
· Experience working with operational, asset, or geospatial data in energy, utilities, or infrastructure sectors (highly desirable, not essential). Familiarity with SCADA, historian, GIS, or IoT sensor data is a strong plus.
· Experience with computer vision (image classification, object detection, segmentation) and/or NLP (text classification, entity extraction, summarisation) in applied settings.
· Knowledge of data engineering fundamentals: SQL, Spark, data pipeline design, and data quality principles. You do not need to be a data engineer, but you must be able to work effectively with them.
· Strong written and spoken English. Comfortable presenting technical results to non-technical stakeholders, writing model documentation, contributing to proposals and commercial presentations, and participating in client-facing calls and workshops.
· Awareness of responsible AI principles, model risk management, explainability techniques (SHAP, LIME), and fairness/bias assessment. Familiarity with the UK AI Regulation White Paper or ISO/IEC 42001 is desirable.
· Genuine interest in energy, utilities, and critical national infrastructure as an application domain. Aligned to nxzen’s purpose and brand.