Data Scientist - HashiCorp Ops

Introduction

A career in IBM Software means you'll be part of a team that transforms our customer's challenges into industry-leading solutions. We are an infinitely curious team, always seeking new possibilities, and dedicated to creating the world's leading AI-powered, cloud-native software solutions. Our renowned legacy creates endless global opportunities for our network of IBMers. We are a team of deep product experts, ensuring exceptional client experiences, with a focus on delivery, excellence, and obsession over customer outcomes. This position involves contributing to HashiCorp's offerings, now part of IBM, which empower organizations to automate and secure multi-cloud and hybrid environments. You will join a team managing the lifecycle of infrastructure and security, enhancing IBM's cloud solutions to ensure enterprises achieve efficiency, security, and scalability in their cloud journey.

Your role and responsibilities

IBM is a global technology and consulting leader helping enterprises transform through hybrid cloud and artificial intelligence. We enable organizations around the world to modernize their business processes, improve decision-making, and accelerate innovation through our open, secure, and scalable technology platforms.

With decades of expertise in data, automation, and AI, IBM helps clients integrate and manage applications while ensuring trust, compliance, and performance. Our technologies empower businesses to unlock value from their data, streamline operations, and deliver smarter, more sustainable outcomes.

We are looking for an experienced Data Scientist to join our Research & Development team for Hashicorp, IBM. The team is focused on building intelligent, usage-centered models that enhance forecasting accuracy, uncover growth opportunities, and accelerate the shift toward AI-driven decision-making in operations.

As a Data Scientist in this team, you will work at the intersection of product usage analytics, machine learning, and forecasting. You will design and implement models that help IBM to understand how customers consume cloud-based infrastructure products and how these patterns translate into revenue, retention, and growth.

What You Will Do

• Usage-based Forecasting: Develop and refine machine learning models that predict revenue from customer product usage (Flexible Consumption), providing accurate short- and long-term forecasts across Terraform, Vault, and other HashiCorp product families.

• Behavioral Modeling: Build models that detect churn risk, contract exhaustion, and opportunity conversion by analyzing usage telemetry, renewal data, and customer lifecycle metrics.

• Predictive & Prescriptive Analytics: Enable FP&A and sales teams with forward-looking insights through predictive and prescriptive analytics that highlight variance drivers, usage health, and potential upsell opportunities.

• AI-driven Financial Intelligence: Collaborate with IBM's AI and Finance teams to integrate models into decision workflows powered by WatsonX and Watson Orchestrate, transforming raw data into actionable business recommendations.

• Data Strategy & Governance:
  Partner with Data Engineering to improve data pipelines, telemetry quality, and forecasting datasets. Establish best practices for model monitoring, interpretability, and continuous improvement.

• 5+ years of experience.
• Strong proficiency in Python (Pandas, NumPy, Scikit-learn, PyTorch, or TensorFlow).

• Expertise in SQL and data modeling.

• Deep understanding of time-series forecasting, predictive modeling, and classification algorithms for churn and revenue prediction.

• Experience building and deploying machine learning pipelines integrated with BI tools (e.g., Sigma, Tableau, or Power BI).

• Working knowledge of data engineering workflows, ETL design, and MLOps for model monitoring and retraining.

• Proven ability to translate usage telemetry and financial data into actionable business insights.

• Experience with usage-based revenue forecasting, contract renewal prediction, and prescriptive analytics for sales and FP&A teams.

• Strong grounding in statistical inference, time-series decomposition, and model validation techniques (cross-validation, backtesting, error analysis).

• Ability to identify key drivers of variance, design counterfactual experiments, and quantify the impact of usage behavior on financial outcomes.

• Experience working cross-functionally with Finance, Product, and Engineering teams to align models with business strategy.

• Familiarity with cloud infrastructure ecosystems (AWS, Azure, GCP, IBM) and SaaS consumption metrics.
• Exposure to AI-driven financial planning or WatsonX/Watson Orchestrate for automation and decision intelligence.