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About This Role
### About SimpliSafe
We're a high\-tech home security company that's passionate about protecting the life you've built and our mission of keeping Every Home Secure. And we've created a culture here that cares just as deeply about the career you're building. Ours is a no ego culture of collaboration and innovation where those seeking their next challenge can find big opportunities and make a huge impact on the lives of all those who we protect. We don't just want you to work here. We want you to grow and thrive here.
We're embracing a hybrid work model that enables our teams to split their time between office and home. Hybrid for us means we expect our teams to come together in our state\-of\-the\-art office on two core days, typically Tuesday, Wednesday, or Thursday – working together in person and choosing where they work for the remainder of the week. We all benefit from flexibility and get to use the best of both worlds to get our work done.
### Why are we hiring?
Well, we're growing and thriving. So, we need smart, talented, and humble people who share our values to join us as we disrupt the home security space and relentlessly pursue our mission of keeping Every Home Secure.
### Role Overview
As Principal Data Scientist, GTM Data Science, you will report to the Senior Director of Data Science and serve as a senior technical leader for growth, marketing, customer lifecycle, and personalization. You will shape the strategy and production systems that help SimpliSafe acquire, retain, and grow subscriber relationships while improving the customer experience.
### What We're Looking For
- Deep expertise in marketing science, customer personalization, lifecycle modeling, and applied machine learning.
- Strong hands\-on experience building and deploying machine learning solutions that have driven measurable business outcomes in production.
- Experience developing models and decision systems for customer acquisition, conversion, retention, segmentation, personalization, lifetime value, and next\-best\-action use cases.
- Strong understanding of experimentation, causal inference, incrementality measurement, and marketing effectiveness.
- Significant experience with MLOps, model deployment, model monitoring, and distributed compute environments.
- Proficiency with Python, SQL, Databricks, AWS SageMaker, Spark, MLflow, and modern cloud data platforms.
- Experience designing scalable data and feature pipelines for production ML systems.
- Ability to operate as a senior individual contributor: setting technical direction, influencing roadmaps, mentoring others, and driving execution across teams.
- Strong communication skills, with the ability to explain complex modeling concepts to technical and non\-technical stakeholders.
- Typically 6\+ years of experience in data science, machine learning, applied statistics, or a related field, or equivalent demonstrated expertise.
### Preferred Qualifications
- Hands\-on experience with advanced ML techniques such as Gradient Boosted Trees, neural networks, and transformer\-based models.
- Experience applying modern AI/ML techniques, including unstructured data or advanced signal integration, in production environments.
- Experience designing or deploying agentic AI solutions that orchestrate models, tools, data, and workflows to automate or augment GTM decision\-making.
- Experience building recommendation, personalization, real\-time decisioning, or next\-best\-action systems.
- Experience in subscription, ecommerce, smart home, security, telecommunications, insurance, fintech, or other customer lifecycle\-driven businesses.
- Advanced degree in Computer Science, Statistics, Machine Learning, Economics, Applied Mathematics, Engineering, or a related quantitative field.
### What Values You'll Share
- Customer Obsessed \- Building deep empathy for our customers, putting them at the core of our work, and developing strong, long\-term relationships with them.
- Aim High \- Always challenging ourselves and others to raise the bar.
- No Ego \- Maintaining a "no job too small" attitude, and an open, inclusive and humble style.
- One Team \- Taking a highly collaborative approach to achieving success.
- Lift As We Climb \- Investing in developing others and helping others around us succeed.
- Lean \& Nimble \- Working with agility and efficiency to experiment in an often ambiguous environment.
### What We Offer
- A mission\- and values\-driven culture and a safe, inclusive environment where you can build, grow and thrive
- A comprehensive total rewards package that supports your wellness and provides security for SimpliSafers and their families *(For more information on our total rewards please* *click here**)*
- Free SimpliSafe system and professional monitoring for your home.
- Employee Resource Groups (ERGs) that bring people together, give opportunities to network, mentor and develop, and advocate for change.
The target annual base pay range for this role is $182,000 to $242,600
This target annual base pay range represents our good\-faith estimate of what we expect to pay for this role. We use a market\-based compensation approach to set our target annual base pay ranges and make adjustments annually. We carefully tailor individual compensation packages, including base pay, taking into consideration employees' job\-related skills, experience, qualifications, work location, and other relevant business factors.
Beyond base pay, we offer a Total Rewards package that may include participation in our annual bonus program, equity, and other forms of compensation, in addition to a full range of medical, retirement, and lifestyle benefits. More details can be found here.
We're committed to fair and equitable pay practices, as well as pay transparency. We regularly review our programs to ensure they remain competitive and aligned with our values.
*We wholeheartedly embrace and actively seek applications from all individuals, no matter how they identify. We are committed to cultivating a diverse and inclusive workplace, and we believe our work is enriched when we incorporate a multitude of perspectives, backgrounds, and experiences. We want everyone who works here to thrive and contribute to not only our mission of keeping every home secure, but also to making our workplace safe and supportive for others. If a reasonable accommodation may be needed to fully participate in the job application or interview process, to perform the essential functions of a position, or to receive other benefits and privileges of employment, please contact* *[email protected]**.*
Salary Context
This $182K-$242K range is above the 75th percentile for Data Scientist roles in our dataset (median: $160K across 258 roles with salary data).
View full Data Scientist salary data →Role Details
About This Role
Data Scientists extract insights and build predictive models from data. In the AI era, many roles now include LLM-powered analytics, automated reporting, and integration with generative AI tools. The role has evolved from 'the person who runs SQL queries' to 'the person who builds AI-powered data products.'
Modern data science roles fall into two camps: analytics-focused (insights, dashboards, experimentation) and ML-focused (building predictive models, recommendation systems, NLP features). The best data scientists can operate in both modes. The AI shift means that even analytics-focused roles now involve building automated insight pipelines using LLMs, going well beyond one-off reports.
Across the 4,317 AI roles we're tracking, Data Scientist positions make up 8% of the market. At SimpliSafe, this role fits into their broader AI and engineering organization.
Data Scientist roles remain in high demand, though the definition keeps shifting. Companies increasingly want candidates who can bridge traditional statistics with modern ML and LLM capabilities. The 'pure insights' data scientist role is consolidating into analytics engineering, while the 'build models' data scientist role is merging with ML engineering.
What the Work Looks Like
A typical week includes: analyzing experiment results for a product feature launch, building a predictive model for customer churn, creating an automated reporting pipeline using LLM-powered summarization, presenting insights to stakeholders, and cleaning data (always cleaning data). The ratio of analysis to engineering varies by company, but expect both.
Data Scientist roles remain in high demand, though the definition keeps shifting. Companies increasingly want candidates who can bridge traditional statistics with modern ML and LLM capabilities. The 'pure insights' data scientist role is consolidating into analytics engineering, while the 'build models' data scientist role is merging with ML engineering.
Skills Required
Python, SQL, and statistical modeling are the foundation. Increasingly, roles want experience with LLMs for data analysis, automated insight generation, and building AI-powered data products. Familiarity with cloud data platforms (Snowflake, BigQuery, Databricks) and ML frameworks (scikit-learn, PyTorch) covers most job requirements.
Experimentation design and causal inference are underrated skills that separate strong candidates. Companies care about whether their product changes cause improvements, and can distinguish causation from correlation. A/B testing methodology, Bayesian statistics, and the ability to communicate uncertainty to non-technical stakeholders are high-value skills.
Good postings specify the data stack, the types of problems you'll work on, and the team structure. Look for companies that differentiate between analytics and ML data science. Vague 'data scientist' postings that list every skill under the sun usually mean the company doesn't know what they need.
Compensation Benchmarks
Data Scientist roles pay a median of $192,890 based on 789 positions with disclosed compensation. Senior-level AI roles across all categories have a median of $227,400. This role's midpoint ($212K) sits 10% above the category median. Disclosed range: $182K to $242K.
Across all AI roles, the market median is $215,000. Top-quartile compensation starts at $266,300. The 90th percentile reaches $320,790. For comparison, the highest-paying categories include AI Safety ($287,500) and Research Engineer ($272,100). By seniority level: Entry: $110,000; Mid: $194,400; Senior: $227,400; Director: $274,554; VP: $241,000.
SimpliSafe AI Hiring
SimpliSafe has 1 open AI role right now. They're hiring across Data Scientist. Based in Boston, MA, US. Compensation range: $242K - $242K.
Location Context
AI roles in Boston pay a median of $210,000 across 166 tracked positions.
Career Path
Common paths into Data Scientist roles include Data Analyst, Statistician, Quantitative Researcher.
From here, career progression typically leads toward Senior Data Scientist, ML Engineer, AI Product Manager.
Start with statistics and SQL. Build a real analysis project on public data that demonstrates insight generation alongside model building. The market values data scientists who can communicate findings clearly to business stakeholders. If you want to move toward ML engineering, invest in software engineering fundamentals and production deployment skills.
What to Expect in Interviews
Interviews combine statistics, coding, and business acumen. SQL is almost always tested, often with complex joins and window functions. Expect a case study round where you're given a business problem and asked to design an analysis plan. Coding rounds focus on pandas, statistical modeling, and visualization. The strongest differentiator is how well you communicate insights to non-technical stakeholders during presentation rounds.
When evaluating opportunities: Good postings specify the data stack, the types of problems you'll work on, and the team structure. Look for companies that differentiate between analytics and ML data science. Vague 'data scientist' postings that list every skill under the sun usually mean the company doesn't know what they need.
AI Hiring Overview
The AI job market has 4,317 open positions tracked in our dataset. By seniority: 138 entry-level, 2,071 mid-level, 1,655 senior, and 453 leadership roles (Director, VP, C-Level). Remote roles make up 15% of the market (635 positions). The remaining 3,657 roles require on-site or hybrid attendance.
The market median for AI roles is $215,000. Top-quartile compensation starts at $266,300. The 90th percentile reaches $320,790. Highest-paying categories: AI Safety ($287,500 median, 34 roles); Research Engineer ($272,100 median, 227 roles); AI Engineering Manager ($244,000 median, 23 roles).
Data Scientist roles remain in high demand, though the definition keeps shifting. Companies increasingly want candidates who can bridge traditional statistics with modern ML and LLM capabilities. The 'pure insights' data scientist role is consolidating into analytics engineering, while the 'build models' data scientist role is merging with ML engineering.
The AI Job Market Today
The AI job market spans 4,317 open positions across 15 role categories. The largest categories by volume: AI/ML Engineer (3,004), Data Scientist (345), AI Software Engineer (309). These three account for the majority of open positions, though smaller categories often have higher per-role compensation because of specialized skill requirements.
The seniority mix tells a story about where AI teams are in their maturity. Entry-level roles (138) are outnumbered by mid-level (2,071) and senior (1,655) positions, reflecting that most companies are past the 'build a team from scratch' phase and need experienced engineers who can ship production systems. Leadership roles (Director, VP, C-Level) total 453 positions, representing the bottleneck between technical execution and organizational strategy.
Remote work availability sits at 15% of all AI roles (635 positions), with 3,657 requiring on-site or hybrid attendance. The remote share has stabilized after the post-pandemic correction. Senior and specialized roles (Research Scientist, ML Architect) are more likely to be remote-eligible than entry-level positions, partly because experienced hires have more negotiating power and partly because these roles require less hands-on mentorship.
AI compensation is structured in clear tiers. The market median sits at $215,000. Top-quartile roles start at $266,300, and the 90th percentile reaches $320,790. These figures include base salary with disclosed compensation. Total compensation (including equity, bonuses, and sign-on) runs 20-40% higher at companies that offer those components.
Category matters for compensation. AI Safety roles lead at $287,500 median, while Prompt Engineer roles sit at $145,000. The spread between highest and lowest-paying categories reflects the premium on specialized technical skills versus broader analytical roles.
The most in-demand skills across all AI postings: Python (2,249 postings), Aws (1,224 postings), Azure (938 postings), Rag (915 postings), Gcp (660 postings), Pytorch (640 postings), Prompt Engineering (624 postings), Kubernetes (559 postings). Python dominates, appearing in the vast majority of role descriptions regardless of category. Cloud platform experience (AWS, GCP, Azure) is the second most common requirement. The newer entrants to the top skills list (RAG, vector databases, LLM APIs) reflect the shift from traditional ML toward generative AI applications.
Frequently Asked Questions
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