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About This Role
Basic Information
Job Name Sr Data Scientist - USA (B) Country United States State NA Date Published 05-Oct-2026 Job ID 47637 Travel You may occasionally be required to travel for business Secondary locations USA Santa Clara Gateway Additional Locations Detroit - Michigan, Houston - Texas, San Francisco - California, New York - New York, Washington - DC This role can be based remotely in United States Looking for more details about our benefits?
Description and Requirements
CareerArc Code CA-MH #LI-MH1 Hybrid: #LI-Hybrid BMC empowers nearly 80% of the Forbes Global 100 to accelerate business value, faster than humanly possible. Our industry-leading portfolio unlocks human and machine potential to drive business growth, innovation, and sustainable success. BMC does this in a simple and optimized way by connecting people, systems, and data that power the world’s largest organizations so they can seize a competitive advantage.
The IZOT product line includes BMC’s Intelligent Z Optimization & Transformation products, which help the world’s largest companies to monitor and manage their mainframe systems. The modernization of mainframe is the beating heart of our product line, and we achieve this goal by developing products that improve the developer experience, the mainframe integration, the speed of application development, the quality of the code and the applications’ security, while reducing operational costs and risks. We acquired several companies along the way, and we continue to grow, innovate, and perfect our solutions on an ongoing basis.
We're looking for a Senior Data Scientist to join our AMI team. Hands-on Machine Learning, statistical modeling, predictive analytics, evaluation, and productionization. Generative AI and agentic systems are owned by AI Engineering; this role stays focused on traditional ML and predictive analytics — with collaboration on GenAI features when modelling expertise adds value.
About the Role You will frame business problems as modeling problems, build and validate predictive and analytical models, and partner with product and engineering to put those models to work where they create measurable value. You apply statistical rigor, experimental design, and reproducible ML practice so models are trustworthy for enterprise use.
Key Responsibilities
- Design and develop Machine Learning solutions using statistical analysis, data mining, and classical/modern ML techniques.
- Build, train, validate, and iterate predictive models (classification, regression, ranking, forecasting, anomaly detection, clustering) against clear business outcomes.
- Perform feature engineering, exploratory analysis, and experiment design to improve model quality and decision usefulness.
- Collaborate with domain experts to understand business requirements and formulate data-driven solutions.
- Define offline and online metrics, holdout strategies, and monitoring signals for model performance and drift.
- Deliver models to enterprise-grade quality: rigorous, validated, reproducible, and ready for mission-critical use.
- Support packaging and deployment of models into production with engineering partners; maintain clear handoffs.
- Partner with AI Engineering and AI Quality when classical models or predictive signals feed GenAI / agentic workflows.
- Explain complex technical concepts and predictive analytics clearly to technical and non-technical audiences.
- Lead a DS technical area (e.g. forecasting, anomaly detection, experimentation standards).
- Set/improve shared modeling, metrics, and experimentation practices across squads.
- Coach data scientists and raise consistent quality bars.
- Partner with engineering/platform on reusable feature, evaluation, and productionization patterns.
Must-Have Skills & Experience
- 8+ years of Data Science experience.
- B.Sc. in Machine Learning, Statistics, Data Science, Physics, or related field — or equivalent education and industry experience.
- Proficiency in Python and data science libraries (Pandas, NumPy, scikit-learn, and PyTorch or TensorFlow)
- Knowledge of statistical analysis, hypothesis testing, experimental design, data mining, and machine learning techniques.
- Experience designing performance metrics and evaluation approaches for predictive ML systems.
- SQL and modern data platforms; data exploration and visualization.
- Familiarity with cloud platforms such as OpenShift (OCP) and AWS.
- Clear communication; ability to work in a multi-tasked, dynamic environment.
- Track record of multi-team influence - standards, metrics, or practices others adopted.
- Experience coaching scientists, not only personal delivery excellence.
- Ability to prioritize a science agenda for an area across initiatives.
Evidence we will look for in hiring:
- Past experience: Improved measurement practices across teams and coached other scientists.
- Delivery evidence: Shared metrics or experimentation standards adopted by multiple teams.
- Shared expectation: Leads a technical area and improves shared practices.
Nice-to-Have Skills
- Python backend APIs (FastAPI, Flask, or similar).
- Exposure to Generative AI / LLMs as a collaborator (not a primary hiring filter).
- MLOps practices (MLflow, Kubeflow, experiment tracking, registries, pipelines).
- Time-series forecasting, causal inference, or anomaly detection in enterprise systems data.
- Agile methodology and Atlassian products (Jira, Confluence).
- M.Sc. in a related field
This Is BMC. Powered by You. At BMC, we don’t do ordinary. Across BMC, our AI teams are building the next generation of agentic AI - capabilities that help enterprises run their most critical systems faster, more resiliently, and with confidence. Our agentic AI empowers autonomous agents to detect, diagnose, and help resolve complex problems with speed, accountability, and human oversight.
We’re not hiring for roles - we’re hiring for impact. You’ll solve hard problems, build smart solutions, and work with people who challenge and champion you - and see your work make a difference for customers running mission-critical systems. #LI-Remote Our commitment to you!
BMC’s culture is built around its people. We have 6000+ brilliant minds working together across the globe. You won’t be known just by your employee number, but for your true authentic self. BMC lets you be YOU!
If after reading the above, You’re unsure if you meet the qualifications of this role but are deeply excited about BMC and this team, we still encourage you to apply! We want to attract talents from diverse backgrounds and experience to ensure we face the world together with the best ideas!
BMC is committed to equal opportunity employment regardless of race, age, sex, creed, color, religion, citizenship status, sexual orientation, gender, gender expression, gender identity, national origin, disability, marital status, pregnancy, disabled veteran or status as a protected veteran. If you need a reasonable accommodation for any part of the application and hiring process, visit the accommodation request page.
BMC Software maintains a strict policy of not requesting any form of payment in exchange for employment opportunities, upholding a fair and ethical hiring process.
The annual base salary range represents the low and high end of the BMC salary range for this position. Actual salaries depend on a wide range of factors that are considered in making compensation decisions, including but not limited to skill sets; experience and training, licensure, and certifications; and other business and organizational needs.
The range listed is just one component of BMC's employee compensation package. Other rewards may include a variable plan and country specific benefits.
At BMC, it is not typical for an individual to be hired at /near the top of the range. A reasonable estimate of the current range is $152,925 - $254,875
Min salary 152,925 Mid point salary 203,900 Max salary 254,875 Min Salary - NEW 152,925 Max Salary - NEW 254,875
Salary Context
This $152K-$254K range is above the 75th percentile for Data Scientist roles in our dataset (median: $161K across 138 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 9,079 AI roles we're tracking, Data Scientist positions make up 4% of the market. At BMC Software, 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 $190,000 based on 1,503 positions with disclosed compensation. Senior-level AI roles across all categories have a median of $229,300. This role's midpoint ($203K) sits 7% above the category median. Disclosed range: $152K to $254K.
Across all AI roles, the market median is $213,000. Top-quartile compensation starts at $263,911. The 90th percentile reaches $318,400. For comparison, the highest-paying categories include AI Safety ($256,000) and Research Engineer ($252,000). By seniority level: Entry: $111,000; Mid: $193,200; Senior: $229,300; Director: $272,300; VP: $235,830.
BMC Software AI Hiring
BMC Software has 6 open AI roles right now. They're hiring across AI/ML Engineer, Data Scientist. Positions span Santa Clara, CA, US, New York, NY, US. Compensation range: $254K - $329K.
Location Context
AI roles in New York pay a median of $221,000 across 3,078 tracked positions. That's 4% above the national median.
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 9,079 open positions tracked in our dataset. By seniority: 106 entry-level, 4,384 mid-level, 3,343 senior, and 1,246 leadership roles (Director, VP, C-Level). Remote roles make up 6% of the market (531 positions). The remaining 8,478 roles require on-site or hybrid attendance.
The market median for AI roles is $213,000. Top-quartile compensation starts at $263,911. The 90th percentile reaches $318,400. Highest-paying categories: AI Safety ($256,000 median, 66 roles); Research Engineer ($252,000 median, 457 roles); AI Engineering Manager ($251,622 median, 55 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 9,079 open positions across 16 role categories. The largest categories by volume: AI/ML Engineer (6,862), AI Product Manager (453), AI Software Engineer (451). 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 (106) are outnumbered by mid-level (4,384) and senior (3,343) 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 1,246 positions, representing the bottleneck between technical execution and organizational strategy.
Remote work availability sits at 6% of all AI roles (531 positions), with 8,478 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 $213,000. Top-quartile roles start at $263,911, and the 90th percentile reaches $318,400. 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 $256,000 median, while Prompt Engineer roles sit at $141,440. 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 (1,649 postings), Aws (898 postings), Azure (723 postings), Rag (671 postings), Gcp (536 postings), Pytorch (434 postings), Claude (410 postings), Kubernetes (406 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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