The Data Analyst will support data science delivery 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. You will apply analytics and statistical methods to real operational problems, contributing to predictive modelling, reporting, and visualisation work under the guidance of senior team members. Based in nxzen’s Global
Capability Centre in Hyderabad or Bangalore, you will report to a Senior Data Scientist or the Director of AI and Analytics, and work as part of the Data Science pillar within nxzen’s Data & AI practice, delivering analytical and predictive solutions for critical national infrastructure operators. This is an early-career, hands-on technical role focused on building strong technical foundations through practical delivery. You will write code, run analyses, build initial predictive models, prepare data, and create visualisations, learning from senior team members as you go.
The role covers a broad span of data analytics and data science activities: exploratory data analysis, feature engineering, data quality assessment, descriptive and diagnostic analytics, predictive modelling, and reporting. Your day-to-day will combine practical data wrangling and visualisation with applied statistical techniques (regression, time-series analysis, hypothesis testing). You will apply machine learning techniques and, where appropriate, generative AI tools, growing your capability with the support of senior colleagues.
You will work closely with senior data scientists, data engineers, and domain consultants, learning from them and contributing meaningfully to client engagements. Success in this role requires strong technical fundamentals, a curious mindset, comfort with messy real-world data, willingness to learn the energy and infrastructure domain, and the ability to communicate analytical results clearly.
Key responsibilities
The Data Analyst will contribute to delivery within the Data Science pillar of nxzen’s Data & AI practice, supporting client engagements across nxzen’s critical national infrastructure client portfolio, combining solid technical fundamentals with practical execution. You will work on client engagements under the guidance of senior team members, collaborating with onshore/offshore consultants, data engineers, and client technical teams.
You will own discrete pieces of analytical work within larger engagements, from data exploration through to model output and visualisation, ensuring your contributions are reliable, well-documented, and aligned to client requirements.
A key focus will be building your technical depth and domain knowledge while delivering measurable analytical value for regulated critical national infrastructure clients. You will contribute to the development and capability growth of the Data Science pillar within nxzen’s Global Capability Centre Data & AI practice.
· Apply predictive analytics and statistical models to critical national infrastructure use cases including asset risk scoring, predictive maintenance, leak and outage prediction, and demand forecasting, under the direction of senior team members.
· Apply core statistical methods (regression, hypothesis testing, basic time-series analysis) to operational data including SCADA historian, smart meter, weather, and IoT sensor streams, building intuition for which methods fit which problems.
· Perform exploratory data analysis, feature engineering, and data quality assessment on operational, asset, geospatial, SCADA, and financial datasets typical of energy and utilities environments. This is a core part of the day-to-day for the role.
· Build dashboards, reports, and visualisations using tools such as Power BI, Tableau, Grafana, or equivalent, to communicate analytical findings to clients and internal stakeholders.
· Support the development of machine learning models, including data preparation, feature engineering, training, and basic validation. Learn deep learning, computer vision, and NLP techniques as opportunities arise on client engagements.
· Contribute to generative AI work where appropriate, including prompt engineering, basic RAG pipelines, and evaluation of LLM outputs, under senior guidance.
· Support deployment of analytical and predictive models, working with senior team members on MLOps pipelines, version control, and monitoring.
· Write SQL queries, support data pipeline development, and work closely with data engineers to ensure analytical work is built on reliable, well-governed data.
· Document your work clearly: methodology, assumptions, model inputs and outputs, results, and limitations. Maintain code in version control with appropriate commenting and follow team coding standards.
· Apply responsible AI principles to your work, including basic explainability checks, awareness of bias, and clear documentation of model assumptions and limitations.
· Participate actively in stand-ups, sprint planning, technical reviews, and team discussions. Ask questions, raise issues early, and contribute ideas to the team.
· Build your knowledge of the energy and infrastructure domain through engagement with senior consultants, technical reading, and direct client exposure.
· Collaborate with onshore delivery leads and client teams, operating effectively in a distributed onshore/offshore model across geographies and time zones.
· Develop your technical skills and stay aware of broader developments in data science, machine learning, and generative AI through self-directed learning, online courses, technical reading, team discussions, and curated learning resources.
· Share what you learn back with the team through informal demos, short writeups, or contributions to internal documentation.
What we’re looking for
We are looking for an early-career data professional with strong technical fundamentals, a genuine appetite to learn, and the discipline to deliver clean, well-documented work. This is a career-building role. We need someone curious, rigorous, and comfortable working through problems with senior guidance. We expect 1 to 3 years of professional experience in data science, data analytics, or related roles. Strong recent graduates with demonstrable Python and SQL skills, internships, capstone projects, or open-source contributions will also be considered. A Bachelor’s degree in a quantitative discipline (Computer Science, Mathematics, Statistics, Physics, Engineering, or similar) is required. A Master’s degree in a relevant field is a plus.
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 findings to colleagues and contributing to client-facing calls. Experience in energy, utilities, or infrastructure is not expected; we will support you in building domain knowledge.
· Solid proficiency in Python for data analysis and modelling, including core libraries: pandas, NumPy, scikit-learn, and a visualisation library (matplotlib, seaborn, or plotly). Exposure to a deep learning framework (PyTorch or TensorFlow) is a plus. Experience with R is also a plus.
· Solid SQL skills for data extraction, transformation, and analysis. Comfortable writing queries across multiple tables and reasoning about query performance.
· Strong grounding in applied statistics, including descriptive and inferential statistics, hypothesis testing, confidence intervals, correlation and regression, probability distributions, sampling, and time-series analysis. Able to choose and justify appropriate techniques for common analytical questions, interpret results correctly, and explain assumptions and limitations to non-technical audiences.
· Experience with data visualisation tools such as Power BI, Tableau, or equivalent. Able to design clear, accurate charts and dashboards that communicate findings effectively to non-technical audiences.
· Working knowledge of machine learning fundamentals: supervised and unsupervised learning, cross-validation, basic feature engineering, model evaluation metrics. Practical experience with at least one end-to-end ML project, academic or professional, is essential.
· Awareness of MLOps concepts: version control (Git), reproducibility, code review, and basic deployment practices.
· Working knowledge of at least one cloud environment (Azure, AWS, or GCP) is essential. Familiarity with cloud ML platforms such as Azure ML, AWS SageMaker, or Databricks is a plus, not essential.
· Awareness of generative AI fundamentals, including prompt engineering, core LLM concepts, and the open-source LLM landscape (Llama, Mistral, Qwen, DeepSeek, Gemma). Practical hands-on use of at least one of these models, or of commercial LLMs such as Claude or ChatGPT, in your work or studies is essential.
· Exposure to operational, asset, or geospatial data in any sector is a plus. No prior energy or utilities experience is expected.
· Any exposure to specialist areas such as computer vision, NLP, or time-series forecasting through projects or coursework is a plus, not a requirement.
· Basic understanding of data pipeline concepts and data quality principles. Familiarity with Apache Spark, Apache Airflow, Azure Data Factory, AWS Glue, Databricks, or dbt is a plus.
· Strong written and spoken English. Comfortable presenting findings to colleagues, writing clear documentation, and participating in client-facing calls.
· Apply responsible AI principles to your work, including basic explainability checks, awareness of bias, and clear thinking about model limitations.
· Curiosity, rigour, attention to detail, and a genuine willingness to learn. Interest in energy, utilities, and critical national infrastructure as an application domain. Aligned to nxzen’s purpose and brand.