Senior Statistical Modeler

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

📋 Description

• Designs and implements analytical and AI solutions that align with business and product goals.

• Conducts intricate analytical tasks throughout the entire modeling lifecycle.

• Utilizes statistical, mathematical, predictive modeling, and business analysis expertise on complex datasets from various sources.

• Establishes and maintains the infrastructure that links internal data sets.

• Develops frameworks for collecting both structured and unstructured data.

• Analyzes business challenges and suggests best practices to enhance products, processes, and services.

• Collaborates with engineering, product, and platform teams.

• Effectively communicates analytical findings to both technical and non-technical stakeholders.

• Provides training and mentorship to junior staff members.

• Acts as a subject-matter expert for clients.

⛳️ Requirements

• Demonstrated experience in Data Science; an advanced academic background, such as a Master’s degree in a relevant field, may compensate for some of the required experience.

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

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

• Proficiency in evaluating model performance using statistical and machine learning metrics along with validation techniques.

• Experience in managing both structured and unstructured data on a large 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.

• Practical experience in developing and executing data science and AI workloads in AWS and Azure cloud environments, encompassing compute, storage, monitoring, and cost-aware 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, such as testing, code quality assessments, and version control processes.

• Capability to independently perform complex analytical tasks within a specified scope.

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

🏝️ Benefits

• Annual incentive bonus.

• Benefits specific to the country.

• Support for disability and accommodation needs.