Senior Statistical Modeler

This is a fully remote position, open to applicants in United States.

📋 Description

• Design and implement analytics and AI solutions that align with business and product goals.

• Conduct intricate analytical tasks and convert established problem statements into scalable machine learning solutions throughout the entire modeling lifecycle.

• Utilize statistical, mathematical, predictive modeling, and business analysis expertise to analyze complex data from diverse sources.

• Build and sustain infrastructure that links internal datasets.

• Develop data collection frameworks for both structured and unstructured data.

• Analyze business challenges and suggest best practices to enhance products, processes, and services.

• Provide training and mentorship to junior team members.

• Act as a subject-matter expert for clients.

• Collaborate with engineering, product, and platform teams.

• Present analytical insights clearly to both technical and non-technical stakeholders.

⛳️ Requirements

• Demonstrated experience in Data Science; advanced academic qualifications such as a Master’s degree in a relevant field may fulfill part of the experience requirement.

• Strong background applying machine learning and statistical methods to real-world challenges.

• Practical experience in developing, assessing, and refining predictive and machine learning models.

• Proficient in evaluating model performance using statistical and machine learning metrics and validation methods.

• Experience handling structured and unstructured data at scale.

• Expertise in Python and/or R, including data science and machine learning libraries such as pandas, NumPy, scikit-learn, XGBoost, and PyTorch.

• Familiarity with SQL and relational or cloud-based data platforms.

• Hands-on experience with developing and executing data science and AI workloads in AWS and Azure, encompassing compute, storage, monitoring, and cost-effective execution.

• Knowledge of contemporary AI frameworks and tools, including LLM-based solutions and retrieval-augmented workflows.

• Experience in training, fine-tuning, or assessing neural network-based models.

• Ability to apply software engineering best practices to data science codebases, including testing, code quality checks, and version control processes.

• Capability to independently carry out complex analytical tasks within a defined scope.

• Proficient in clearly communicating technical concepts to non-technical audiences.

🏝️ Benefits

• Annual performance-based incentive bonus.

• Country-specific benefits.

• Disability accommodations and assistance throughout the hiring process.