Staff Data Scientist, Machine Learning

Accountabilities • Lead the end-to-end roadmap for ML and AI initiatives, from opportunity sizing and experimentation through evaluation, deployment, and measurement of business impact.

• Design and implement real-time anomaly detection models and scalable data-processing systems that identify product usage, adoption, and behavioral signals.

• Apply predictive and prescriptive modeling to business challenges such as churn, revenue forecasting, price sensitivity, marketing mix optimization, segmentation, funnel optimization, and acquisition quality.

• Develop AI agents and automated analytical workflows using LLMs, RAG, embeddings, semantic layers, and agent orchestration frameworks.

• Analyze identity and security-related signals, including unusual login patterns, MFA behavior, authentication velocity, and deviations from historical user activity.

• Build robust ML pipelines supporting both real-time and asynchronous inference, with strong validation, monitoring, and production reliability.

• Establish pragmatic standards and governance for model development, LLM evaluation, observability, security, performance, and AI service cost management.

• Translate complex technical findings into clear business recommendations, communicating trade-offs and insights effectively to senior stakeholders.

• Collaborate across functions to align stakeholders with competing priorities, create clarity in ambiguous environments, and influence strategic outcomes.

• Mentor analysts and other data professionals while developing reusable tools, frameworks, and practices that improve predictive problem-solving across the organization.

• Continuously investigate unfamiliar business domains to identify meaningful signals, features, and appropriate modeling approaches.

Requirements

• 8+ years of professional experience spanning data science, analytics, applied machine learning, and/or LLM-based solutions, with a strong record of delivering business-facing outcomes.

• Advanced expertise in Python, SQL, statistical analysis, and commonly used machine learning frameworks.

• Strong experience with MLOps, including feature engineering, model deployment, inference, monitoring, and production ML pipelines.

• Hands-on experience with LLM APIs, RAG, embeddings, semantic layers, and AI-agent or orchestration frameworks.

• Demonstrated ability to develop real-time anomaly detection models and large-scale data-processing solutions.

• Experience applying predictive and prescriptive analytics to commercial problems such as churn, forecasting, pricing, marketing effectiveness, and customer acquisition.

• Understanding of identity-related threat signals and methods for detecting deviations from historical user behavior is highly valuable.

• Proven ability to design and build AI-powered analytical systems while balancing relevance, performance, reliability, and service costs.

• Strong understanding of segmentation, root cause analysis, funnel optimization, and acquisition quality scoring.

• Experience working in fast-moving, data-first organizations or startup environments where machine learning directly influences business outcomes.

• Excellent business acumen, analytical reasoning, and communication skills, with the ability to explain complex concepts and recommendations clearly.

• Comfortable working independently in ambiguous environments, taking problems from initial exploration through implementation and measurable outcomes.

• Demonstrated curiosity and strategic thinking, with the ability to move between detailed technical analysis and broader business strategy.

• Experience with modern data engineering practices, including data governance, transformation, and orchestration, is preferred.

• Familiarity with recurring-revenue B2B SaaS business models is preferred.

• Strong English communication skills, both written and verbal, are required.

• Candidates must be located in India and authorized to work in India.

Benefits

• Fully remote position within India.

• Remote-first working environment designed to support distributed teams across multiple countries.

• Opportunity to work on challenging ML, LLM, AI-agent, and data analytics problems with meaningful business impact.

• Collaboration with experienced, multidisciplinary teams in a fast-paced SaaS environment.

• Opportunities to influence technical direction, build reusable AI capabilities, and contribute to strategic initiatives.

• Strong opportunities for professional growth, knowledge sharing, mentorship, and technical leadership.

• Inclusive and collaborative culture that values diverse perspectives, innovation, and individual contributions.

• Opportunity to work closely with senior leadership and influence product and business decisions.

• The source description does not specify a salary range or detailed benefits package.

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