Senior Data Scientist

BMC Software

. Lead the design and development of machine learning and data science solutions for complex product and customer problems.

Posted 11/9/2026full-timeSanta Clara • California • United StatesSenior💰 $152,925 - $254,875 per yearWebsite

Core Competencies

Role fit

Core Competencies

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Demonstrates expertise in designing and developing machine learning and data science solutions, with a strong focus on Python and modern data science frameworks. Capable of translating complex product and customer problems into actionable data science initiatives while effectively communicating findings to diverse stakeholders.

Highest-signal resume keywords

Expert-Level PythonMachine Learning Model DevelopmentData Science Frameworks (Scikit-Learn, PyTorch, TensorFlow)LLM and GenAI ExperienceEvaluation Framework Design

ATS Keywords

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Applicant Tracking System Keywords

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Hard Skills

Machine LearningData ScienceStatistical AnalysisModel ValidationPerformance EvaluationData ExplorationExperimentationModel Performance UnderstandingFine-Tuning TechniquesProduction AI Considerations

Soft Skills

CommunicationProblem-SolvingCollaborationAdaptabilityDecision-Making

Tools & Technologies

AWS SageMakerAzure MLVertex AIKubernetesOpenAIAnthropicLangGraphLangChainLlamaIndexXGBoost

Industry Keywords

GenAILLMAgentic AIData Science SolutionsProduct Impact Measurement

Tech Stack

Tools & technologies

AWSAzureKubernetesPythonPyTorchScikit-LearnTensorflow

About the role

Key responsibilities & impact

Lead the design and development of machine learning and data science solutions for complex product and customer problems.
Take data science initiatives from problem definition and experimentation through validation and production adoption.
Build machine learning models where appropriate.
Apply statistical analysis and experimentation to measure product impact.
Explore large and complex datasets to identify signals that can improve product capabilities.
Develop robust approaches to model validation and performance evaluation.
Define meaningful metrics and evaluation frameworks for traditional ML and GenAI-powered capabilities.
Work with product and engineering teams to translate customer and business problems into data science solutions.
Use Python and modern data science tooling to build reproducible modelling and experimentation workflows.
Evaluate emerging techniques across machine learning, LLMs and GenAI and determine where they provide genuine product value.
Communicate complex analytical findings clearly to engineering, product and senior leadership.
Identify opportunities and move forward without waiting for perfectly defined requirements.
Work alongside the VP of Engineering, VP of AI and product leadership to define how data science and machine learning are used across the platform.
Build BMC's next-generation Agentic AI platform from 0–1.

Requirements

What you’ll need

5+ years of professional experience in Data Science, Machine Learning or a closely related field, with experience operating at senior or principal level.
Expert-level Python and strong experience with the modern Python data science ecosystem.
Hands-on experience with common ML and data science frameworks such as scikit-learn, PyTorch, TensorFlow, XGBoost or equivalent.
Strong understanding of model performance, bias, explainability, drift and the limitations of machine learning systems.
Experience applying Data Science within GenAI, LLM or Agentic AI products.
Hands-on experience building or integrating LLM-powered applications.
Experience with LLM APIs and frameworks such as OpenAI, Anthropic, LangGraph, LangChain or LlamaIndex.
Strong hands-on experience designing and implementing evaluation frameworks for LLM and Agentic AI systems, including measuring quality, reliability and performance.
Experience partnering closely with software engineers, product managers and other technical stakeholders.
Ability to explain complex modelling decisions and findings to both technical and non-technical audiences.
Comfort operating in ambiguity and making pragmatic decisions with incomplete information.
Experience with fine-tuning techniques including LoRA, QLoRA or preference optimisation (nice to have).
Experience deploying ML or AI workloads through AWS SageMaker, Azure ML, Vertex AI, Kubernetes or equivalent (nice to have).
Understanding of production AI considerations including inference, observability, latency, cost, guardrails and scalability (nice to have).

Benefits

Comp & perks

Variable plan
Country specific benefits
Equal opportunity employment
Reasonable accommodation during the application and hiring process