Lead data science team for AI Search recommendations at Peec AI. Responsible for end - to - end ML lifecycle and technical leadership.
About the role
- Senior Data Scientist at Guardian utilizing AI/ML to drive enterprise-wide innovation. Collaborating with cross-functional teams to create advanced data science solutions.
Responsibilities
- Lead use case/workstream with junior data scientists
- Support use case development that includes initial data exploration, project/sample design, reception and processing of data, performing analysis and modeling to creation of final report/presentation
- Data wrangling/data matching/ETL to explore a variety of data sources, gain data expertise, perform summary analyses and prepare modeling datasets
- Utilizing advanced statistical and AI/ML techniques to create high-performing predictive models and creative analyses to address business objectives and partner needs
- Identification of source data and data quality checks both in model/solution development and in production
- Packaging of model/solution and deployment in cooperation with Data Engineers and MLOps
- Implement new statistical or other mathematical methodologies as needed for specific models or analysis
- Propose innovative ways to look at problems through using data mining and data visualization
- Work with stakeholders throughout the organization to identify opportunities for leveraging company data to drive business solutions
- Present information using data visualization techniques; communicate results and ideas to key decision makers
- Ensure data accuracy and consistent reporting by performing regular data quality control, prepare and maintain reports, and troubleshoot data anomalies
Requirements
- PhD with 2+ years of experience, Master's degree with 4+ years of experience in Statistics, Computer Science, Engineering, Applied mathematics or related field
- 3+ years of hands-on ML modeling/development experience
- Solid understanding of data analysis and statistical modeling
- Knowledge of a variety of machine learning techniques (clustering, decision tree, bagging/boosting artificial neural networks, etc.)
- Demonstrated track records in experimental design and executions
- Hands-on experience with data wrangling including fuzzy matching and regular expression, distributed computing and applying parallelism to ML solutions
- Strong programming skills in Python
- Solid background in algorithms and a range of ML models
- Excellent analytical and problem-solving abilities with superb attention to detail
Benefits
- Health insurance
- 401(k) matching
- Flexible work arrangements
- Professional development opportunities
- Paid time off
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