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The Analyst I, Data Science gains experience in collaborating with business partners to develop predictive analytic solutions that enable data driven strategic decision making. This role utilizes data science techniques to manipulate large structured and unstructured data sets, identify patterns in raw data, and develop models to predict the likelihood of a future outcome and/or to optimize business solutions.
This level focuses on gaining industry knowledge, development of predictive analytics techniques, and obtaining experience in storytelling with data.
- Researches and develops predictive analytic solutions including generalized linear models (GLM), time series forecasting and media mix modeling, and other statistical and machine learning models
- Mines large data sets using sophisticated analytical techniques to generate insights and inform business decisions
- Identifies and tests hypotheses, ensuring statistical significance through experimental design and builds predictive models for business application
- Translates quantitative analyses and findings into accessible visuals for non-technical audiences and provides clear view into interpreting the data
- Engages customers to understand business problems and customize analytic and predictive model solutions to business needs
- Leads projects with low to moderate complexity
- Responsible for smaller components of projects of high complexity
- Regularly engages with the data science community and participates in crossfunctional working groups
- Works with a broader team using GIT or other code management tools using AWS big data solutions, like S3 and Snowflake
- Telecommuting permitted up to 20%.
The position requires a Masters Degree in Statistics, Applied Mathematics, Economics, Actuarial Science or a related field, plus six (6) months of relevant experience in the job offered or a related occupation. The position also requires demonstrable experience with each of the following:
- Implementation of forecasting models using arima, exponential smoothing, and other Bayesian methods
- Implementation of survival models for forecasting customer churn and quantifying the impacts
- Building financial fraud models, maintaining and evaluating fraud campaigns
- End to end modeling, specifically building process including data collecting and cleaning, feature extraction and reduction, missing value handling, robust predictive modeling measurement and methodologies such as cross validation and confidence interval and out of sample validation
- Sizing and making proposals for systems enhancements and developing the monitoring plan including building Rshiny dashboards for tracking and providing a post-hoc cost benefit analysis
- Machine learning methodologies such as Gradient Boosted Trees, Random Forest, and Support Vector Machines
- SAS and R programming to automate data and modeling procedures and provide tracking through building custom Rshiny applications and dashboards
- Telecommuting permitted up to 20%.
To apply, please visit https://jobs.libertymutualgroup.com/, select Search Jobs, enter job requisition #2021-34352 in the Job ID or Keywords field, and submit resume. Alternatively, you may apply by submitting a resume via e-mail to [email protected]. Reference requisition number in subject of e-mail.