Top Remote Data & Analytics Jobs in Spokane, WA

As a Senior Machine Learning Engineer, you will design data pipelines, develop machine learning models, and collaborate with various teams to integrate ML capabilities into products. You will also train others, conduct experiments to validate outcomes, and serve as a thought leader in data-driven innovations.

The Senior Data Engineer will design and architect data pipelines and ETL processes, maintain data lakehouse solutions, optimize data strategies across cloud platforms, mentor junior engineers, and ensure the high availability of data infrastructure while collaborating with data scientists for advanced analytics initiatives.

As a Principal GRC Analyst at Red Canary, you will lead governance, risk, and compliance initiatives, ensuring that policies and controls are effective and compliant. You will conduct audits, manage vendor risk assessments, and support the sales team regarding security compliance terms. Your role involves overseeing security awareness training and business continuity exercises.

The Vice President of Business Development will drive growth in the Data & AI practice by developing go-to-market strategies, qualifying and closing multi-year engagements, and leveraging relationships with platform partners and sales professionals. This role focuses on data modernization, analytics, digital transformation, and implementing data-driven solutions.

The Applied Scientist will enhance customer-service provider matching through machine learning and optimization, develop predictive models, analyze marketplace dynamics, and manage monetization mechanics to foster marketplace growth at Thumbtack.

As a Senior Manager of Analytics at Spring Health, you will lead the embedded analyst program, collaborating with the Medical Affairs team to improve data infrastructure, tools, and dashboards. You will manage three analysts, refine operational processes, and ensure the team meets its key performance indicators (KPIs).

The Senior Data Scientist will focus on fraud analytics, applying statistical modeling and machine learning to detect fraud in consumer-facing applications. Responsibilities include analyzing large datasets, developing predictive models, and creating real-time monitoring systems to identify and combat fraudulent activities.