Develop and validate predictive and prescriptive models using advanced analytical techniques, including machine learning, statistical modeling, optimization, and simulation.
Perform rigorous feature engineering, iterative model improvement, and implement A/B testing strategies.
Deploy models into production environments adhering to best practices in DevOps, including robust documentation, version control, and continuous integration.
Conduct regular monitoring and maintenance of models, addressing performance degradation and proactively updating based on new data or evolving business needs.
Collaborate with Data Engineers and MLOps professionals to ensure seamless integration and operational reliability of deployed models.
Actively engage with cross-functional team members including business analysts, data engineers, architects, product management, and subject-matter experts to understand business objectives and align analytics solutions to strategic goals.
Mentor junior team members, conduct code reviews, and contribute to team skill development.
Evaluate emerging technologies and analytical tools, recommending adoption when beneficial.
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Bachelors degree in Data Science, Computer Science, Statistics, Economics, Information Systems, or a related quantitative field required; masters degree or relevant professional certifications and extensive practical experience highly preferred.
3-5 years of practical experience in advanced analytics, predictive modeling, and feature engineering.
Strong programming proficiency in Python, SQL, and familiarity with scripting languages such as Bash or PowerShell.
Experience deploying models using DevOps methodologies (CI/CD, containerization, version control with Git).
Demonstrated experience with common data science methodologies: decision trees, neural networks, regression, time-series analysis, PCA, and ensemble modeling.
Effective communication skills to clearly present complex insights to technical and non-technical stakeholders.
Understanding of data governance, privacy, and ethical considerations in data handling.
Familiarity with cloud services, particularly Oracle Cloud Infrastructure (OCI), as well as experience in multi-cloud environments (AWS, Azure, or GCP). Experience with model-drift detection and automated monitoring solutions.
Proven ability in working effectively within cross-functional teams.
Benefit packages for this role will start on the 31st day of employment and include medical, dental, and vision insurance, as well as HSA, FSA, and DCFSA account options, and 401k retirement account access with employer matching. Employees in this role are also entitled to paid sick leave and/or other paid time off as provided by applicable law.