Senior Data Scientist Job Boise area,Idaho USA,IT/Tech

Role & responsibilities

Develop, implement, and optimize machine learning models to solve complex business problems and improve decision-making.
Perform exploratory data analysis (EDA), feature engineering, statistical analysis, and predictive modelling on large datasets.
Build and evaluate supervised and unsupervised learning models, including classification, regression, clustering, and time-series forecasting.
Apply deep learning, NLP, and Generative AI techniques to develop innovative data-driven solutions.
Translate business requirements into analytical approaches, actionable insights, and measurable outcomes.
Deploy, monitor, and maintain production-grade machine learning models in collaboration with data engineering and software engineering teams.
Develop scalable data science workflows and pipelines using Python, SQL, and cloud-based technologies.
Conduct hypothesis testing, A/B testing, experimentation, and statistical validation to measure model effectiveness.
Communicate analytical findings, model performance, and recommendations to technical and non-technical stakeholders.
Mentor junior data scientists, conduct code reviews, and promote best practices in model development, documentation, and reproducibility.
Ensure data quality, model reliability, explainability, privacy, and compliance with relevant governance standards.
Stay updated with emerging machine learning techniques, AI technologies, and industry best practices.

Preferred candidate profile

5 to 10 years of relevant experience in Data Science, Machine Learning, Advanced Analytics, or a related field.
Bachelor's or master's degree in Computer Science, Data Science, Artificial Intelligence, Statistics, Mathematics, Engineering, or a related quantitative discipline.
Strong proficiency in Python, SQL, statistics, probability, data manipulation, and exploratory data analysis.
Hands-on experience developing, validating, and deploying machine learning models using libraries such as Scikit-learn, Tensor Flow, or PyTorch.
Strong understanding of feature engineering, model selection, hyperparameter tuning, cross-validation, and model evaluation metrics.
Experience with NLP, deep learning, Generative AI, LLMs, and RAG architectures is an advantage.
Working knowledge of cloud platforms such as AWS, Azure, or GCP, along with MLOps, Docker, CI/CD, and model monitoring.
Experience with big data technologies, data pipelines, and distributed processing frameworks such as Apache Spark is preferred.
Proven ability to independently manage end-to-end data science projects and deliver measurable business impact.
Strong analytical thinking, problem-solving, communication, and stakeholder management skills.
Ability to collaborate with cross-functional teams and mentor junior team members.
Experience in production model deployment, experimentation, and translating complex analytical results into business recommendations is highly desirable.