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Ava Moore

From Statistics Undergraduate to Senior Data Scientist

Last updated: Jan 24, 2025

Data Scientist in Data Science & AI · Sydney, Australia

Senior data scientist sharing the real career journey behind every dashboard. From statistics graduate to ML production, with the failed experiments included. Written for anyone considering analytics or AI as a path.

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Career Timeline

Education2013-2015
Statistics Undergraduate
University of British Columbia
Discovered I loved statistics by accident — one professor made p-values make sense. Built a small dropout-prediction project for the placement cell.
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ContextDiscovered I loved statistics by accident — one professor made p-values make sense. Built a small dropout-prediction project for the placement cell.
ChallengesMath anxiety from school showed up again. Friends in engineering thought stats was a soft option.
LearningsStatistics is the grammar of every data conversation later. Excel is underrated; SQL is underrated; both are still here.
Skills acquired
ProbabilityregressionRExcelhypothesis testingterm papers
Higher Studies2015-2018
M.Sc. Statistics Student
University of British Columbia
Switched from theory to applied. Internship summer between years was my first real-world dataset — messy, incomplete, full of human errors.
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ContextSwitched from theory to applied. Internship summer between years was my first real-world dataset — messy, incomplete, full of human errors.
ChallengesRealising most of the job is cleaning data, not modelling. Tutorials never tell you that.
LearningsIf your data is wrong, your model is theatre. Spend an extra day on EDA — it saves a week of explaining bad results.
OutcomeFinal thesis on credit risk got me a campus offer.
Skills acquired
Bayesian methodsPythonscikit-learnSQLtime seriesA/B testing
First Job2018-2021
Data Analyst
TCS
Started in analytics consulting for retail and BFSI clients. Half my time was building decks, half cleaning CSVs.
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ContextStarted in analytics consulting for retail and BFSI clients. Half my time was building decks, half cleaning CSVs.
ChallengesClients sometimes wanted answers, not analysis. Saying "we do not have enough data" was unpopular.
LearningsAn analyst who can explain trade-offs to a non-technical VP is worth more than one who can train an XGBoost from memory.
OutcomePromoted from Analyst to Senior Analyst in 22 months. Built a churn dashboard still used by the client.
Skills acquired
SQLdashboards (TableauPower BI)stakeholder communicationbusiness KPIs
Career Switch2021-Present
Senior Data Scientist
Microsoft
Moved from consulting to a product team. Owning recommendation experiments and the metrics that decide what to ship.
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ContextMoved from consulting to a product team. Owning recommendation experiments and the metrics that decide what to ship.
ChallengesConsulting taught me to deliver decks; product taught me to deliver experiments. Different rhythm, different ego.
LearningsMost ML wins are simpler features and better evaluation, not bigger models. Beware of leakage; beware of "lift" without confidence intervals.
OutcomeLed an experiment that lifted activation by 8% for a region with 30M users. Mentored two junior scientists into mid roles.
Skills acquired
PyTorchMLOpsfeature storesexperiment designmentoring
AdviceIf your only tool is a model, every problem looks like a hackathon. Learn the business first.

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Ava Moore

Career Timeline

Statistics Undergraduate

M.Sc. Statistics Student

Data Analyst

Senior Data Scientist

Reflections & Questions from Readers