Data Analyst
Location: Hyderabad
Shift: US business hours overlap
Experience: 3 to 6 years
The role
We are hiring a Data Analyst for a US healthcare business. The data already lands in BigQuery and thepipelines are looked after by the engineering team. Your job starts after that: work out what data we actually have, what is worth paying attention to, and what it is telling us.This is an analysis and insight role with a data science lean. You will be expected to find your own wayaround the warehouse, pull and shape data yourself, build the dashboards that matter, and then gofurther than the dashboard. Why did a number move. Is it correlation or causation. What happens next.
You will work directly with US leadership, including the CEO, and be asked open questions rather thanhanded specifications.
Responsibilities
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Understanding the data
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Explore what data exists across BigQuery and other sources, and build a working view of what isavailable, how reliable it is, and where the gaps are
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Identify which data is genuinely useful for the questions the business is asking
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Recommend what should be monitored on an ongoing basis, and why Pull and manipulate data in raw form without waiting on the engineering team
Analysis and insight
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Turn data into clear observations and recommendations, not just charts
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Investigate why a metric moved: form a hypothesis, test it against the data, report the result in
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plain language
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Distinguish correlation from causation, and be explicit about which one you have
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Run cohort, segmentation, trend and outlier analysis across clinical, operational, member and growth data
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Deliver ad hoc analysis for US leadership on short turnaround
Dashboards and monitoring
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Build dashboards in Looker, Looker Studio, Power BI or Tableau covering the metrics that matter
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Own them after launch: monitor them, catch anomalies, breaks and trend changes before
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stakeholders do
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Retire or rebuild reporting that nobody uses or nobody trusts
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Testing and predictive work
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Design and run tests to establish what is driving a result
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Apply statistical methods appropriately: significance testing, regression, confidence intervals
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Build forecasting and predictive models where they add value, and be honest about their limits
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Measure the impact of business changes after they are made
Working with the business
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Turn vague business questions into answerable data questions
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Say when the data cannot support the conclusion someone wants, and what it would take to get
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there
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Present findings to non-technical executives in writing and live
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Work with the data engineering team on definitions, quality issues and new data requirements
Must have
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3 to 6 years in data analysis, business intelligence or data science
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Strong SQL: joins, window functions, CTEs, aggregation across large tables
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Comfortable working in BigQuery, or another cloud warehouse with the ability to pick BigQuery up
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quickly
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Python for analysis: pandas, numpy, and at least one of scikit-learn, statsmodels or similar
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Applied statistics: hypothesis testing, significance, regression, and knowing when each applies
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Hands on with at least one BI tool: Looker, Looker Studio, Power BI or Tableau
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Can walk through an analysis they ran end to end: the question, the hypothesis, the method, and
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what the business did as a result
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Self directed. Comfortable being given a business problem rather than a ticket
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Strong spoken and written English, comfortable presenting to US stakeholders
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Available for US hours overlap
Good to have
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US healthcare data: EHR, claims, eligibility, pharmacy or PBM feeds
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Experience with predictive modelling or forecasting in a production business setting
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Experimentation and A/B testing background
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Exposure to finance or unit economics data: cost per member, cohort retention, revenue drivers
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dbt or Dataform familiarity, enough to read and extend existing models
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Experience as the first or only analyst in a business
