Pharmaceutical Sales Force Effectiveness Analyst | Data Scientist | Educator

Technical Skills: R, Python, SQL, Power BI, Power Query, Amazon Redshift, PostgreSQL, Advanced Excel (PivotTables, Macros/VBA), DAX, AWS, Salesforce, ArcGIS, Tableau, Jira, Confluence, Collibra, Git

Resume

Education

Bachelor of Science (Data Science and Genetics & Genomics), The University of Sydney (2022 – 2024)
- Dalyell Scholar, Dean’s Entry Scholarship, Science Top of the State Scholarship, Sydney Scholars Award, Dean’s List of Academic Achievement 2024
- Public Education Foundation Award

Work Experience

Junior Sales Force Effectiveness Analyst

AstraZeneca, Macquarie Park, NSW
Apr 2025 – Present

Act as the central analytics partner to senior commercial leaders, providing timely and strategic insights across sales performance, HCP engagement and executional excellence metrics.
Design, maintain and continuously improve automated dashboards and reporting tools using Power BI, SQL and PowerQuery.
Architect and maintain end-to-end data pipelines using Amazon Redshift for high-volume querying, Python for automated ETL and workflow orchestration and R for statistical modelling.
Own the integrity of commercial data pipelines by validating data sources (e.g. IQVIA, Nostra, Ex-Factory, PBS, Wholesaler) and ensuring consistency across enterprise platforms, informing decision-making for field performance, resourcing and patient access programs.
Support and deliver key commercial initiatives including Sales Targeting, Incentive Design, Kicker Programs and Sales Champion Awards, ensuring structures are analytically grounded and aligned to regional performance goals.
Drive adoption of SFE systems and analytical tools by delivering tailored training and ongoing support to internal stakeholders, enhancing the commercial acumen and digital fluency of field teams and head office personnel alike.
Lead analytical contributions to strategic programs such as lead scoring, microjourney optimisation, customer segmentation, sampling activity and Veeva CRM insights, enabling precision engagement and improved ROI across omnichannel strategies.
Support executive-level initiatives including business planning cycles, customer experience simplification, and meeting effectiveness, using hypothesis-driven reporting and longitudinal metrics to guide strategic refinement.

Casual Academic

School of Mathematics and Statistics, University of Sydney
Jan 2024 – Present

Led engaging tutorials and workshops for over 250 undergraduate students in DATA1001, supporting foundational learning in data wrangling, probability theory, visualisation and hypothesis testing.
Developed and facilitated hands-on activities using real-world datasets to enhance conceptual understanding and practical application of statistical and programming principles.
Assessed major student projects with a focus on code readability, reproducibility, and analytical rigour, delivering clear and constructive feedback to support student learning and growth.
Mentored junior demonstrators in teaching strategies, marking practices and classroom management – several of whom successfully progressed to tutor roles.
Collaborated closely with the unit coordinator, lecturers and fellow tutors to maintain alignment across marking rubrics, lesson pacing and student support strategies.

Key Account Manager (on behalf of AbbVie)

DKSH Healthcare Australia, Sydney, NSW
Jan 2025 – Apr 2025

Promoted AbbVie’s curative hepatitis C treatment across Australia, driving patient recall and prescribing confidence among primary care HCPs.
Led recall audit initiatives, training in-field sales team members in MedicalDirector and Best Practice software to identify at-risk patients.
Used xBERT and Veeva CRM platforms to manage HCP interactions, track engagement and optimise outreach strategies.
Strategically prioritised DNSR clinics, targeted ‘unicorn’ clinics and refined affinity and potential profiling to maximise field efficiency and coverage.
Generated and qualified over 50 high-value leads, streamlining handover to the in-field team and significantly boosting national prescribing opportunities.
Built strong rapport with prescribers, enhancing trust, education, and patient screening for hepatitis C.
Received the 2025 DKSH Inspire Award in recognition of exceptional sales performance, innovation and collaborative excellence.

Program Coordinator (on behalf of Novo Nordisk)

DKSH Healthcare Australia
Jul 2024 – Dec 2024

Supported over 500 patient interactions via email, inbound calls using 3CX, and Chatlio live chat for Novo Nordisk’s WegovyCare program, ensuring exceptional service and timely resolution of inquiries.
Utilised a CRM system to manage patient records, communications, and workflow, ensuring continuity and quality of care.
Conducted 100% accurate pharmacovigilance reporting to the Therapeutic Goods Administration.
Communicated directly with healthcare professionals, including pharmacists, general practitioners and specialists, building rapport to support patient care and ensure treatment adherence.
Ensured strict adherence to patient confidentiality and handling of sensitive medical information in compliance with the Privacy Act and Australian Privacy Principles.
Collaborated with cross-functional teams to implement process improvements and contributed to the updated Standard Operating Procedure, streamlining program operations and ensuring consistency in patient support delivery.

President

Sydney University Science Society, University of Sydney
Oct 2023 – Oct 2024

Led the largest student society in Australia, representing over 3,100 students and overseeing an executive team of 15.
Spearheaded a $176,000 operational budget, securing increased base funding from the Faculty of Science and raising $20,000 for equity initiatives, enabling 100+ students to attend high-cost events free of charge.
Established SciSoc’s first-ever Bylaws, introducing policies on equity, environmental sustainability, privacy and treasury governance, ratified by general vote.
Introduced the Asset Purchase Program (APP) to manage long-term infrastructure and improve operational sustainability, now a permanent fixture in funding strategy.
Revitalised SciSoc’s publication portfolio, launching a First Year Guide for Orientation – the first publication in over 20 years – with 2,300+ unique readers.
Directed a comprehensive rebrand of marketing strategy, growing SciSoc’s social media following by 3,000+ and reactivating LinkedIn for monthly professional development posts.
Negotiated and delivered new partnerships with RedBull, GradReady and Paratus Clinical, securing both monetary and in-kind sponsorships.
Mentored subcommittee members, several of whom successfully progressed into executive roles, strengthening the society’s internal talent pipeline.
Introduced internal governance reforms to support the amalgamation of smaller societies into Science Society’s structure, improving their financial viability and access to institutional support.

Certifications

Medicines Australia CEP
Advanced First Aid and CPR
Mental Health First Aid
Responsible Service of Alcohol
Valid Working With Children Check

Awards & Achievements

99.75 ATAR
First in NSW HSC Investigating Science
2022 STANSW Young Scientist Award

Projects

Multi-Omics Analysis of E. coli Adaptation to Blood Environments

Tools: R, ggplot2, MetaboAnalyst, STRINGdb, LC-MS/MS, KEGG, UniProt

This project investigated the adaptive metabolic and proteomic responses of Escherichia coli strain B36 when grown in human blood versus nutrient broth, with implications for sepsis research.

Conducted proteomic profiling to identify differentially expressed proteins using Manhattan distance rankings.
STRINGdb analysis of upregulated proteins revealed clusters in bacterial chemotaxis, flagellar biosynthesis, and the TCA cycle, while downregulated proteins mapped to outer membrane proteins, histidine metabolism, and aromatic amino acid biosynthesis.
Targeted LC-MS/MS metabolomics identified 95 unique metabolites. PCA revealed strong separation between control and blood-grown cells.
MetaboAnalyst pathway enrichment identified major shifts in glutathione metabolism, TCA cycle, and sulphur amino acid biosynthesis.
Integrated findings across omics layers showed coordinated regulation of energy production (TCA & glyoxylate shunt), chemotaxis (CheA & aspartate), and adaptive nutrient acquisition (LacZ & galactose).
These changes reflect metabolic flexibility and immune evasion strategies relevant to pathogenicity in bloodstream infections.

CRISPR-Cas9 Editing of CCR5: Sequence, Structure, and Bioethical Analysis

Tools: Geneious Prime, TMHMM, NCBI, Sequence Alignment, Protein Translation, Bioethics Literature Review

This project explored the molecular and ethical dimensions of CRISPR-Cas9 gene editing in the controversial 2018 experiment by Dr He Jiankui. The study focused on editing the CCR5 gene, which encodes a receptor involved in HIV entry into CD4+ T cells.

Conducted multiple sequence alignments of wild-type CCR5 with ∆32, Lulu, and Nana mutant alleles using Geneious Prime, identifying nucleotide and amino acid changes.
Translated gene variants into protein sequences and predicted transmembrane domains using the TMHMM Hidden Markov Model, revealing truncated or structurally altered proteins in edited alleles.
Identified likely Protospacer Adjacent Motif (PAM) sequences that guided Cas9 editing and examined the fidelity and limitations of PAM consensus (5’-NGG-3’).
Analysed downstream effects of frameshift mutations, comparing edited variants to the natural ∆32 allele associated with HIV resistance.
Evaluated the gene repair mechanisms (NHEJ vs HDR) used and the resulting indels, frameshifts, and implications for protein function.
Assessed the ethical justification and scientific validity of the experiment, discussing off-target effects, unintended phenotypes, and the broader risks of germline gene editing.

Urban Resource Equity in Greater Sydney: A Multi-Metric SA2 Ranking Model

Tools: Python (Pandas, GeoPandas, Scikit-learn), PostgreSQL, Kepler.gl, PCA, Rank-Based Scoring, Spatial Analysis

As part of a group project for DATA2901, we developed a scoring model to evaluate how well-resourced different SA2 regions in Greater Sydney are across key service domains.

Integrated and preprocessed 9 diverse datasets including income, public transport, polling stations, schools, businesses, childcare, and libraries using Python and SQL.
Built a normalised relational database with GIST indexes for spatial joins and efficient querying of geospatial data.
Designed a refined scoring function using capped z-scores weighted by Principal Component Analysis (PCA) to account for feature importance and limit the impact of outliers.
Created a rank-based alternative scoring model, validating the original z-score model with a Spearman’s rank correlation of 0.985.
Developed interactive visualisations including a Kepler.gl 3D map and static heatmaps to highlight spatial inequality in resource distribution.
Discovered strong clustering in well-resourced regions around the CBD, supported by a Pearson’s correlation coefficient of -0.654 between resource score and distance from the city centre.
Incorporated income correlation analysis, finding a weak positive relationship (r = 0.293) between median income and resource access.
Addressed urban planning implications, such as spatial mismatch and equity of infrastructure in suburban areas.

Predicting Alcohol Content in Vinho Verde Wines

Tools: R, Stepwise Regression, PCA, Cross-Validation, R Shiny, Variable Selection (mplot)

This project aimed to improve the precision of alcohol content labelling on Portuguese white Vinho Verde wines using data from 4,898 samples. Under Australian regulations, alcohol must be labelled within ±1.5% ABV, motivating our goal to predict alcohol content using 11 physicochemical properties.

Conducted data cleaning and exploratory analysis on key features such as density, residual sugar, sulphates, and acidity.
Built and refined multiple regression models using stepwise selection (AIC) and stability plots via the mplot R package.
Identified density and residual sugar as the most predictive variables. Final model included 7 features and explained 91% of the variance in alcohol content.
Verified model assumptions including linearity, homoscedasticity, normality, and independence using VIF, residual plots, and QQ-plots.
Validated model performance using 10-fold cross-validation, comparing full and reduced models based on MAE and R². The reduced model was chosen to minimise overfitting.
Discussed ethical and practical considerations, including the risk of underreporting and importance of compliance with national labelling standards.

Assessing Coral Recovery and Reassembly in the Great Barrier Reef

Client: Dr. Ana Paula da Silva
Course: STAT3926 – Statistical Consulting
Date: June 2024

This project investigated the post-disturbance recovery and reassembly of coral communities on the Great Barrier Reef using ecological and environmental datasets. Recovery was assessed as the time taken for coral cover to return to pre-disturbance levels, while reassembly was evaluated using Bray-Curtis similarity to measure changes in community composition.

We used a combination of:

Wilcoxon rank sum tests to assess differences in recovery time by coral morphology, region, and shelf location
Logistic regression models to evaluate spatial and morphological predictors of successful reassembly
Generalised additive models (GAMs) to explore the relationship between sea surface temperature (SST) and disturbance frequency under RCP 4.5 and RCP 8.5 scenarios

Key findings:

Median recovery time was one year, but varied significantly by coral type and location
83% of reefs successfully reassembled, though no significant predictors were identified
SST increases are projected to raise disturbance frequency, especially under RCP 8.5

This multi-model approach revealed the complexity of coral recovery and highlighted the need for incorporating broader environmental and biological factors in resilience assessments.

Forecasting Study Load at the University of Sydney

Client: Susie Chee
Course: STAT3926/STAT4026 – Statistical Consulting
Date: May 2024

This consulting project involved developing a forecasting model to estimate Equivalent Full-Time Student Load (EFTSL) at the University of Sydney, in response to recent changes in government policy affecting international student enrolments.

A series of faculty-specific linear models were built, each incorporating predictors such as year, semester, fee type (CSP, DFEE, IFEE), and a dummy variable for COVID-19 impacts. A total of 24 models were created across eight faculties. These models were used to forecast 2024 EFTSL figures and assess whether the University was on track to meet its budget targets.

Key contributions and outcomes:

High-performing models: Strong model fits for many CSP and IFEE faculty models.
COVID-19 & semester effects: Impact of COVID-19 and Semester 2 enrolments were explicitly quantified.
Forecast vs. budget: Projected 2024 income of $2.42 billion, exceeding previous years and suggesting the University is on track to meet budget targets.
Performance evaluation: Compared model accuracy to existing university forecasts using MAE and annual percentage change. While the model slightly underperformed in some areas, it showed promise for refinement.
Limitations: Small dataset (2018–2023) and inability to model recent behavioural changes in student enrolments due to policy shifts. Future model improvements should include more historical data and additional predictors.

This project demonstrates the use of predictive modelling to support university-level financial and enrolment planning in a changing policy landscape.

Enhancing Modelling Approaches for Analysing Chalinolobus gouldii Vocalisation Behaviour

Client: Magic Mei-Ting Kao
Course: STAT3926/STAT4026 – Statistical Consulting
Date: May 2024

This consulting project focused on supporting a PhD candidate studying Chalinolobus gouldii (Gould’s wattled bat) vocalisation behaviour across various habitats. The client sought confirmation and enhancement of her statistical modelling workflow, particularly regarding the appropriateness of Generalised Linear Mixed Models (GLMMs) with nested random effects.

Key contributions and outcomes:

Model selection and validation: Confirmed the suitability of a Poisson GLMM for modelling overall bat activity (count data), given the data’s hierarchical structure (e.g., recordings nested within sites and dates).
Diagnostics and fit assessments: Verified model assumptions with residual diagnostics (using DHARMa), overdispersion tests, and zero-inflation checks—no issues were detected, supporting model robustness.
Interpretation of fixed effects:
- Temperature: Significantly increased bat activity (+9% per °C, p < 0.01).
- Reproductive periods: Activity declined during mating (−33%) and pregnancy (−35%), both statistically significant.
- Anthropogenic influence: Significantly reduced activity (−27%, p < 0.05).
Random effects analysis: Identified considerable variability in bat activity across sites, highlighting key hotspots (e.g., BLH_w4) and quieter zones (e.g., CTNP_duck).
Reproducibility and scalability: Developed a well-documented, flexible, and reproducible GLMM workflow, suitable for application to other response variables like social call activity.

Recommendation:
Continue using the enhanced Poisson GLMM framework. It is robust, interpretable, and well-suited to the client’s research aims. Further extensions can explore Bayesian methods (e.g., brms) for sensitivity analysis and richer inference.

This project exemplifies applied ecological modelling, statistical diagnostics, and the development of reproducible tools for interdisciplinary academic research.