What it pays
Government survey numbers — not estimates, not ads.
Half of all Statisticians in the U.S. earn more than $105,650 a year — the middle 80% land between $64,000 and $174,050. About 29,030 people in the U.S. do this work. Figures are for the U.S. occupation group “Statisticians”. (U.S. Bureau of Labor Statistics survey, published 2025.) In India, Professionals earn about ₹38,298 a month on average — around ₹4.6 lakh a year (government PLFS survey via ILOSTAT, occupation-family figure).
$105,650typical pay / year
29,030people in this work
$174,050+top 10% earn
₹4.6 lakha year in India (family avg)
Think you get this job?Six quick questions on how it really works — with a hint and the reason behind every answer.
Test yourself →The work, task by task
These are the real jobs-to-be-done, not a wish list. Each task shows where we found it, and the prompt underneath is written for that exact task.
Analysing6
Apply statistical methods to data
+Apply the agreed statistical tests to last season’s play-by-play and player-tracking tables, check…
Apply statistical methods to data
+Apply the agreed statistical tests to last season’s play-by-play and player-tracking tables, check assumptions and missing values, produce the cleaned dataset and a short memo explaining model choices for the coaching analytics lead by Thursday noon.
The tools that do the workIBM SPSS StatisticsESCOjob descriptionsO*NETWikipedia
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Analyze data to identify trends
+Run trend analyses on the past three seasons of team and player metrics, flag emerging patterns in scoring,…
Analyze data to identify trends
+Run trend analyses on the past three seasons of team and player metrics, flag emerging patterns in scoring, usage and defensive efficiency, produce charts that highlight week-to-week shifts and send the briefing to Head Coach Martin and scouting by Monday morning.
The tools that do the workMicrosoft Exceljob descriptionsO*NETWikipedia
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Collect data
+Collect game logs, sensor feeds and official box scores for the last ten games, reconcile player IDs and…
Collect data
+Collect game logs, sensor feeds and official box scores for the last ten games, reconcile player IDs and timestamps, produce one validated table ready for analysis and upload it to the shared project folder by end of day today.
The tools that do the workLinuxESCOjob descriptionsWikipedia
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Produce statistical reports and visualizations
+Produce the weekly statistical report with five visualizations: scoring distribution, lineup net ratings,…
Produce statistical reports and visualizations
+Produce the weekly statistical report with five visualizations: scoring distribution, lineup net ratings, shot-chart heatmaps, minutes per rotation and injury-adjusted projections, include a two-page executive summary for the performance director by Friday 3pm.
The tools that do the workMicrosoft OfficeMicrosoft Exceljob descriptionsO*NETWikipedia
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Collect and organize data for analysis
+Ingest the season’s raw tracking and box-score feeds, standardize variable names, build the master…
Collect and organize data for analysis
+Ingest the season’s raw tracking and box-score feeds, standardize variable names, build the master game-by-game table with derived metrics (per-possession rates, rolling splits), and document the pipeline for the analytics team before deployment on Monday.
The tools that do the workApache Sparkjob descriptionsO*NETWikipedia
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Develop and apply statistical principles
+Design and run the validation experiments for the new lineup-adjusted rating: specify assumptions, choose…
Develop and apply statistical principles
+Design and run the validation experiments for the new lineup-adjusted rating: specify assumptions, choose estimators, bootstrap confidence intervals, compare to last year’s ratings and write the methods section for the technical notebook by next Wednesday.
The tools that do the workIBM SPSS Statisticsjob descriptionsO*NET
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The daily work14
Identify statistical patterns
+Scan the season play-by-play and boxscore tables, calculate per-possession efficiency, on/off differentials,…
Identify statistical patterns
+Scan the season play-by-play and boxscore tables, calculate per-possession efficiency, on/off differentials, and sequence-level momentum shifts, flag patterns with p<0.05 and produce a ranked table of anomalies with sample charts for the coaching staff.
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Gather data
+Pull every game’s play-by-play, lineup, and injury logs for the last two seasons, clean and normalise player…
Gather data
+Pull every game’s play-by-play, lineup, and injury logs for the last two seasons, clean and normalise player and team IDs, fill missing timestamps, and deliver one master dataset ready for modeling with notes on gaps and data quality issues.
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Process data
+Clean and normalise the season tracking files, join play-by-play to lineup rosters, flag missing player IDs…
Process data
+Clean and normalise the season tracking files, join play-by-play to lineup rosters, flag missing player IDs and injury entries, then produce a single tidy dataset ready for weekly modelling and QC by Thursday morning.
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Think analytically
+Review the last three years of shot, possession and opponent-adjusted metrics, test for structural shifts…
Think analytically
+Review the last three years of shot, possession and opponent-adjusted metrics, test for structural shifts after the March trade deadline, and write a two-paragraph assessment of whether the sample can be pooled for this season's projections.
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Perform data analysis
+Run the planned feature set through the prebuilt regression and clustering pipelines, export model outputs…
Perform data analysis
+Run the planned feature set through the prebuilt regression and clustering pipelines, export model outputs and residuals, and summarise top predictors and suspicious outliers for the modelling meeting on Tuesday.
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Apply statistical analysis techniques
+Apply the chosen statistical tests to the season split samples, compute confidence intervals and effect sizes…
Apply statistical analysis techniques
+Apply the chosen statistical tests to the season split samples, compute confidence intervals and effect sizes for minutes, usage and shooting splits, and prepare the figures to support the coaching debrief on Wednesday.
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Apply scientific methods
+Design the pre-registered experiment: state the hypotheses about lineup changes, define treatment and control…
Apply scientific methods
+Design the pre-registered experiment: state the hypotheses about lineup changes, define treatment and control periods, list required covariates and the stopping rule, then save the protocol for peer review before next Friday.
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Write work-related reports
+Compile the weekly analytics report: executive summary of three key findings, table of player workload and…
Write work-related reports
+Compile the weekly analytics report: executive summary of three key findings, table of player workload and injury flags, visual of trend in team efficiency, and a one-paragraph recommendation for Sunday lineup selection.
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Assess reliability of source information
+Assess the reliability of the match feeds from StatStream and LeagueOps for last season: check timestamp…
Assess reliability of source information
+Assess the reliability of the match feeds from StatStream and LeagueOps for last season: check timestamp consistency, player ID mapping, missing-event rates by game, and flag any sources with over 2% discordant records before I publish the official dataset.
The tools that do the workIBM SPSS StatisticsMicrosoft Exceljob descriptionsO*NET
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Evaluate and describe data utility
+Evaluate the utility of the tournament dataset for predictive work: summarise feature completeness, variance,…
Evaluate and describe data utility
+Evaluate the utility of the tournament dataset for predictive work: summarise feature completeness, variance, season-to-season stability, and which fields I can use for player-performance models versus which need enrichment or should be dropped.
The tools that do the workMicrosoft ExcelIBM SPSS Statisticsjob descriptionsWikipedia
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Use software like R, Python, SAS, SQL
+Prepare the scripts and queries to ingest the last two years of play-by-play into our analysis environment,…
Use software like R, Python, SAS, SQL
+Prepare the scripts and queries to ingest the last two years of play-by-play into our analysis environment, include data cleaning steps for duplicate plays, standardise team and player IDs, and output tidy tables ready for R or Python modeling.
The tools that do the workMicrosoft AccessMicrosoft Exceljob descriptionsWikipedia
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Build models and perform hypothesis testing
+Build the performance model and associated hypothesis tests for home advantage: specify dependent variables,…
Build models and perform hypothesis testing
+Build the performance model and associated hypothesis tests for home advantage: specify dependent variables, create covariates for travel and rest, run significance tests, and produce effect sizes with confidence intervals for coaching review.
The tools that do the workIBM SPSS Statisticsjob descriptions
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Liaise with management to define data needs
+Meet with head of analytics and the GM to define next season’s data needs: bring examples of derived metrics,…
Liaise with management to define data needs
+Meet with head of analytics and the GM to define next season’s data needs: bring examples of derived metrics, outline what raw events we need added, and agree which delivery timelines unlock weekly performance reports.
The tools that do the workMicrosoft Officejob descriptions
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Create charts
+Create the visual set for tonight’s brief: trend lines for scoring rates by period, heat maps of shot…
Create charts
+Create the visual set for tonight’s brief: trend lines for scoring rates by period, heat maps of shot locations for the top ten players, and a one-page summary chart the coaches can read at a glance.
The tools that do the workMicrosoft ExcelESCO
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Built on public evidence: O*NET®, ESCO, Wikipedia, U.S. Bureau of Labor Statistics, ILOSTAT. All sources & licenses
Built on public evidence: O*NET®, ESCO, Wikipedia, U.S. Bureau of Labor Statistics, ILOSTAT. All sources & licenses