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Sr. Data Scientist
Ref No.: 25-01089
Location: Bellevue, Washington
Position Type:Contract
Role name: Sr. Data Scientist
Location: Bellevue, US (onsite)
Contract Role

Role and responsibilities:
• 7+ years in data science, analytics, or related fields.
• Proven track record of delivering data-driven business solutions and deploying ML models in production.
• Experience leading projects or mentoring junior team members is preferred.
• Strong proficiency in Python, R, or similar programming languages.
• Deep knowledge of machine learning frameworks (Scikit-learn, TensorFlow, PyTorch, XGBoost).
• Expertise in statistical analysis, data visualization, and predictive modeling.
• Experience with SQL, NoSQL databases, and data engineering pipelines.
• Familiarity with cloud platforms (AWS, Azure, GCP) and big data tools (Spark, Hadoop) is a plus.
• Strong analytical thinking, problem-solving skills, and business acumen.
• Excellent communication skills to present technical insights to stakeholder
Analyze complex datasets to identify trends, patterns, and business opportunities.
• Build, validate, and deploy machine learning models for predictive, classification, or recommendation purposes.
• Perform feature engineering and data preprocessing to improve model performance.
• Stay up-to-date with emerging techniques in machine learning, deep learning, NLP, and AI.
• Apply advanced statistical and AI methods to solve business challenges.
• Experiment with new algorithms, frameworks, and tools for improved accuracy and scalability.
• Collaborate with Data Engineers and DevOps teams to deploy models into production.
• Monitor model performance, retrain models as needed, and ensure data quality.
• Document model assumptions, limitations, and decision-making processes.
• Work closely with product managers, business stakeholders, and engineering teams to align data science solutions with business needs.
• Mentor junior data scientists and provide technical guidance on modeling, coding, and best practices.
• Present insights and findings to technical and non-technical stakeholders effectively.
• Ensure adherence to data governance, privacy, and compliance policies.
• Address ethical considerations in AI and data modeling.