Sarah T***

CAREER SUMMARY

Senior Data Scientist with 11 years of experience applying statistical modelling, machine learning and advanced analytics to solve complex business and operational problems. Experienced across the full data science lifecycle, including data exploration, statistical analysis, feature engineering, predictive modelling, experimentation, model evaluation and deployment. Strong background developing analytical solutions across structured and unstructured datasets, with experience translating complex analytical findings into practical recommendations for technical and business stakeholders. Experienced in collaborating with data engineers, machine learning engineers, software developers, product managers and senior business leaders to deliver data-driven solutions. Strong technical expertise in Python, SQL, machine learning, statistical modelling and data visualisation, with a focus on building reliable models and measurable business outcomes.

EDUCATION

Monash University, Melbourne VIC
Master of Data Science, February 2011 – December 2012

EXPERIENCE

Advanced Analytics Australia, Melbourne VIC — Senior Data Scientist, January 2019 – Current

Lead data science projects from problem definition and data exploration through modelling, validation, deployment and ongoing performance monitoring.
Work closely with product managers, business stakeholders, data engineers and software engineers to translate business problems into analytical and machine learning solutions.
Develop predictive models for classification, regression, forecasting, customer behaviour, risk assessment and operational optimisation.
Perform exploratory data analysis and statistical analysis to identify patterns, relationships, trends and opportunities within complex datasets.
Design and engineer features from large structured and unstructured datasets to improve model performance and analytical outcomes.
Evaluate machine learning models using appropriate statistical and business metrics, including cross-validation and model performance analysis.
Apply statistical modelling, hypothesis testing and experimentation techniques to support evidence-based product and business decisions.
Develop analytical frameworks and automated data pipelines to improve the efficiency and reproducibility of data science workflows.
Collaborate with machine learning and software engineering teams to productionise analytical models and integrate them into business applications.
Monitor model performance and data quality in production environments, identifying model degradation and opportunities for improvement.
Communicate complex analytical findings through clear reports, visualisations and presentations for technical and non-technical stakeholders.
Conduct A/B testing and experimentation to measure the impact of product, customer and operational changes.
Provide technical guidance and mentoring to data scientists and analysts, including support with modelling approaches, analytical methods and best practices.
Lead technical reviews of data science methodologies, model assumptions, analytical results and project recommendations.
Present data-driven insights and strategic recommendations to senior management and cross-functional leadership teams.

Data Intelligence Group, Melbourne VIC — Data Scientist, January 2015 – December 2018

Developed statistical and machine learning models to support business forecasting, customer analytics and operational decision-making.
Collected, cleaned, transformed and analysed large datasets using Python, SQL and statistical techniques.
Conducted exploratory data analysis to identify trends, anomalies, relationships and potential business opportunities.
Developed predictive models using regression, classification, clustering and ensemble learning techniques.
Performed feature engineering and model optimisation to improve predictive performance and model reliability.
Designed and analysed A/B tests and controlled experiments to evaluate business and product initiatives.
Created data visualisations and analytical dashboards to communicate findings to business and technical stakeholders.
Worked closely with data engineers to improve data quality, data pipelines and availability of analytical datasets.
Supported the deployment of machine learning models into production environments and monitored model performance.
Conducted statistical analysis and hypothesis testing to validate business assumptions and support decision-making.
Documented analytical methodologies, model assumptions, experiment results and technical recommendations.
Participated in Agile development processes, technical reviews and cross-functional project meetings.
Assisted junior analysts and data scientists with analytical methods, Python development and statistical modelling.

Business Analytics Services, Melbourne VIC — Data Analyst, January 2013 – December 2014

Analysed business and customer data to identify trends, performance patterns and opportunities for improvement.
Developed SQL queries and data transformation processes to prepare datasets for analysis and reporting.
Conducted descriptive and statistical analysis to support business planning and operational decision-making.
Created dashboards, reports and visualisations using analytical and business intelligence tools.
Worked with business stakeholders to define analytical requirements and translate questions into measurable data problems.
Performed data quality checks and investigated inconsistencies across multiple data sources.
Supported forecasting and performance analysis using statistical techniques and historical data.
Prepared analytical reports and presented findings to internal stakeholders.
Collaborated with data and technology teams to improve reporting processes and data availability.
Maintained documentation covering data sources, analytical methodologies and reporting processes.

TECHNICAL SKILLS

Data Science: Predictive modelling, statistical modelling, machine learning, exploratory data analysis, feature engineering, model evaluation, forecasting, optimisation

Machine Learning: Regression, classification, clustering, decision trees, random forests, gradient boosting, XGBoost, ensemble methods, anomaly detection

Statistics: Hypothesis testing, statistical inference, probability, regression analysis, experimental design, A/B testing, time-series analysis

Programming: Python, SQL, R

Python Libraries: Pandas, NumPy, Scikit-learn, SciPy, Matplotlib, Seaborn, XGBoost, TensorFlow, PyTorch

Data & Cloud: AWS, Azure, Databricks, Apache Spark, data warehouses, ETL pipelines, large-scale data processing

Databases: PostgreSQL, MySQL, SQL Server, MongoDB, Snowflake, BigQuery

Visualisation: Power BI, Tableau, Matplotlib, Seaborn, analytical dashboards, data storytelling

MLOps & Deployment: MLflow, model deployment, model monitoring, feature pipelines, model versioning, production analytics

Professional Skills: Technical leadership, analytical thinking, problem-solving, stakeholder management, mentoring, strategic thinking, communication, presentation and data storytelling

Education

2011-02 to 2012-12
Master of Data Science
Monash University, Melbourne VIC

Skills

Data Science | Machine Learning | Statistical Modelling | Predictive Analytics | Advanced Analytics | Python | SQL | R | Pandas | NumPy | Scikit-learn | SciPy | XGBoost | TensorFlow | PyTorch | Feature Engineering | Predictive Modelling | Regression | Classification | Clustering | Forecasting | Time-Series Analysis | Hypothesis Testing | A/B Testing | Experimental Design | Exploratory Data Analysis | Data Visualisation | Power BI | Tableau | Data Storytelling | Apache Spark | Databricks | AWS | Azure | Snowflake | BigQuery | Data Warehousing | ETL | MLOps | MLflow | Model Evaluation | Model Deployment | Model Monitoring | Statistical Analysis | Business Intelligence | Technical Leadership | Stakeholder Management | Mentoring

Occupation: Senior Data Scientist

Experience: 11 years

Location: Victoria, Australia

Skills: Python, SQL, Analytics

Experience: 11 years

Daily rate: AUD 300

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