Okay, I've searched for Senior Data Scientist roles in Seattle, WA that are full-time, require 7+ years of experience in SaaS or enterprise software, demand strong skills in Python and machine learning, and offer a salary between $140,000 and$160,000 annually. I have found 58 positions matching your criteria.

Sure. Here's the analysis:

Job Analysis:

The Senior Data Scientist role at Acumatica is fundamentally designed to bridge the gap between raw data and actionable business insight, operating at a strategic intersection of analytics, engineering, and cross-functional collaboration. This position demands not only deep technical expertise in advanced analytics, machine learning, and cloud data platforms but also a strong capacity to understand and translate dynamic business challenges into data-driven solutions. The incumbent will work closely with diverse business teams to uncover opportunities beyond traditional reporting, pushing toward predictive and prescriptive analytics that drive competitive advantage. Success in this role means being a catalyst for data literacy and innovation, influencing decision-makers at all levels while mentoring emerging talent. The role’s complexity lies in balancing hands-on model development and strategic oversight, requiring excellent communication skills to simplify complex analyses and align data architecture with analytical goals. Candidates should be prepared to operate with relative autonomy, managing ambiguity in new use cases and evolving business priorities, while ensuring scalable, compliant, and value-driven data science practices.

Company Analysis:

Acumatica is an ambitious and growth-focused cloud ERP provider catering primarily to small and mid-sized enterprises through a flexible, customer-centric product suite. Positioned as a market innovator rather than a legacy player, Acumatica values high energy, simplicity, and outstanding customer service, cultivating a casual but mission-driven culture. For a Senior Data Scientist, this cultural environment suggests that agility, collaboration, and a proactive mindset are key to thriving. The company emphasizes both technical excellence and a people-oriented approach, meaning this role will have ample opportunity to impact cross-functional teams and the broader business ecosystem. The data science team is likely embedded within a collaborative organizational structure, where the incumbent will have visibility with leadership given the strategic nature of the role in shaping analytics roadmaps. The position aligns with company goals to scale operations intelligently and to innovate how data propels business growth and customer value, reflecting Acumatica’s forward-looking approach to cloud ERP solutions. Candidates should expect a fast-moving environment where their technical leadership and business acumen will be critical to delivering measurable impact.

Absolutely. Here are some mock interview questions that could come up:

• Behavioral: Tell me about a time when you had to translate complex business requirements into a data science solution. How did you ensure alignment with stakeholders?
• Behavioral: Describe an occasion when you identified an opportunity to use machine learning to solve a business problem that was not initially recognized by others. What steps did you take to develop and deploy the solution?
• Behavioral: How have you mentored junior data scientists or analysts in the past? Share how you approach building a strong analytics team.
• Behavioral: Give an example of how you navigated ambiguity or conflicting priorities when working across multiple business functions.
• Technical: Can you walk me through your process for designing and implementing forecasting or classification models using cloud data platforms like Snowflake or Databricks?
• Technical: How do you approach collaborating with data engineering teams to ensure that data pipelines and architectures support advanced analytics needs?
• Technical: Describe your experience with data governance and compliance frameworks. How do you embed these considerations into your data science workflows?
• Technical: What is your experience integrating machine learning models into a lakehouse environment? What challenges have you faced and how did you overcome them?
• Situational: Imagine a business leader requests an urgent dashboard with key metrics, but the underlying data is incomplete or inconsistent. How would you handle this request?
• Situational: If you identified that a current analytics process was reactive and not driving proactive business decisions, how would you propose and implement a shift toward predictive analytics?
• Situational: Suppose your team is debating between two differing modeling approaches, each favored by different stakeholders. How do you facilitate a resolution that supports both business and technical objectives?
• Company Fit/Motivation: What attracts you specifically to Acumatica’s mission and culture as a cloud ERP innovator for small and midsize businesses?
• Company Fit/Motivation: How do you see your experience aligning with Acumatica’s focus on simplicity, customer-centricity, and high-energy collaboration?
• Company Fit/Motivation: Acumatica emphasizes freakishly friendly customer service and making a difference. How do you see data science contributing to these values?
• Questions for Interviewer: How does the data science team currently collaborate with product and business units to prioritize analytics projects?
• Questions for Interviewer: What are the biggest challenges Acumatica faces in scaling its advanced analytics capabilities over the next 12 months?
• Questions for Interviewer: How is success measured for the Senior Data Scientist role within the first year, and what are the key milestones you expect?