Data Analyst ( Data Science )

About the role

This position supports Arcadia's AI-powered energy intelligence platform, concentrating on utility data that enterprise teams leverage for bill management, procurement, and sustainability initiatives. The hire will operate within India-based remote or hybrid arrangements that adhere to L2 level guidelines. You will transform complex utility datasets into clear insights that drive business decisions and operational efficiency. The role demands a strong partnership with cross-functional business stakeholders to ensure analytical solutions align with strategic objectives. You will be responsible for validating data quality and ensuring the integrity of analytical outputs used by enterprise clients. This position bridges the gap between technical data capabilities and commercial energy management needs. The work directly contributes to reducing carbon footprints and eliminating redundant processes in energy workflows.

Key facts

What you'll do

Investigate utility data pipelines to identify anomalies and ensure high-quality inputs for analysis.

Develop and maintain SQL queries that extract, transform, and validate large volumes of energy usage data.

Construct and refine dashboards using Python to visualize consumption patterns and support strategic decisions.

Perform statistical analysis to evaluate energy trends and support experimental design for efficiency initiatives.

Clean and standardize disparate datasets, addressing missing values, duplicates, and inconsistencies systematically.

Collaborate with the Applied AI team in India to translate business requirements into technical data specifications.

Implement data validation frameworks that proactively detect anomalies in utility billing and procurement datasets.

Explore and model machine learning techniques relevant to Natural Language Processing within energy contexts.

Document analytical methodologies and findings to ensure clarity and reproducibility for technical audiences.

Optimize query performance to handle large datasets efficiently and reduce processing latency.

Communicate insights to non-technical stakeholders through clear reports, visualizations, and presentation materials.

Support the development of platforms that enable enterprise teams to manage bill management, procurement, and sustainability goals.

Conduct exploratory data analysis to uncover hidden patterns and generate hypotheses for further investigation.

Demonstrate ownership of analytical products from initial design through deployment and ongoing iteration.

Requirements

2-3 years of experience in data analytics, business analytics, data science, statistics, or a related field.

Strong Python programming skills for developing clean, maintainable scripts for data processing, automation, and analysis.

Strong SQL skills with proficiency in joins, aggregations, window functions, common table expressions, and query optimization.

Experience working with large datasets and preparing high-quality data through cleaning, transformation, validation, standardization, and investigation of missing values, duplicates, inconsistencies, and anomalies.

Solid understanding of exploratory data analysis, descriptive statistics, and basic experimental design and evaluation.

A detail-oriented approach that questions why data looks incorrect before assuming it is correct.

Strong verbal and written communication to present findings clearly to technical and non-technical stakeholders through presentations, reports, visualizations, and documentation.

Ability to work independently on well-defined tasks while collaborating effectively with senior team members.

Demonstrated interest in machine learning, NLP, or Generative AI, with willingness to learn and apply these technologies.

Practical notes

The role is hybrid with a remote-first policy, and may require occasional office visits.

Comprehensive coverage includes accident policy and life insurance for listed family members.

Typical interview steps

Data interviews commonly include a SQL or coding exercise, a statistics question, and a case study. Candidates may be asked to design a metric, interpret an experiment, or build a small model. Some companies give a take-home analysis. Expect questions about past projects and the business impact of your work. Interviewers often evaluate how you communicate uncertainty and business impact, not only the math. Bringing a clean write-up of a past analysis to the interview is well received.

Good to know

The role focuses on utility data platforms used by large enterprises for billing, procurement, and sustainability reporting.

SQL and Python are central tools for building and maintaining analytical pipelines.

Machine learning and NLP are emerging areas relevant to evaluation and production workflows.

Efforts support reducing carbon footprints and eliminating redundant processes.

Questions to ask

Worth asking in any interview: how the team measures success, who the role works with daily, what the onboarding looks like, and what the company is trying to achieve this year. Asking what past hires did well is a strong final question. Keep the list short and pick the questions that matter most to you.

Career growth

Data careers grow toward senior analyst, staff data scientist, or data engineering lead. Many professionals specialize in machine learning, analytics, or infrastructure. Cross-functional work with product and engineering teams becomes more important at senior levels. The field changes quickly, so continuous learning is part of the job. Professionals who can translate numbers into decisions tend to advance fastest.

About the company

Arcadia Group Ltd was a British multinational retailing company headquartered in London, England. It was best known for being the previous parent company of British Home Stores (BHS), Burton, Dorothy Perkins, Debenhams, Evans, Miss Selfridge, Topman, Topshop, Wallis and Warehouse.