Data Scientist

About Us

Since 1989, SHI International Corp. has helped organizations change the world through technology. We’ve grown every year since, and today we’re proud to be a $16 billion global provider of IT solutions and services.

Over 17,000 organizations worldwide rely on SHI’s concierge approach to help them solve what’s next. But the heartbeat of SHI is our employees – all 7,000 of them. If you join our team, you’ll enjoy:

Our commitment to diversity, as the largest minority- and woman-owned enterprise in the U.S.

Continuous professional growth and leadership opportunities.

Health, wellness, and financial benefits to offer peace of mind to you and your family.

World-class facilities and the technology you need to thrive – in our offices or yours.

Job Summary

We’re looking for a curious, driven, and analytically strong Data Scientist to join our newly formed Growth Strategy & Deal Operations team. You’ll play a pivotal role in helping us develop data models and decision intelligence tools that support smarter pricing, improved deal governance, and greater commercial performance across SHI’s enterprise business.

This is a hands-on role ideal for early-career professionals who want to apply machine learning, statistical modelling, and data science techniques in real business applications — including pricing optimization, margin forecasting, deal scoring, and opportunity analysis.

Role Description

Include but not limited to;

AI/ML Model Development:

Support development of machine learning models for pricing optimisation, margin prediction, and customer segmentation.

Build and maintain deal scoring and prioritisation models to assess strategic value and commercial risk.

Apply techniques like regression, clustering, time series forecasting, and classification to commercial datasets.

Data Analysis & Insight:

Analyse historical pricing, win/loss, vendor incentive and cost data to uncover trends and inform pricing strategy.

Collaborate with Pricing Strategy, Competitive Intelligence, and Deal Desk teams to identify and test use cases.

Create dashboards and visualisations that enable real-time insights into pricing performance and deal behavior.

Implementation & Enablement:

Help translate models into tools or decision aids for use by sales, finance, and pricing teams.

Work with the Business Intelligence and Data Engineering teams to ensure access to clean, structured data pipelines.

Participate in A/B testing and pilot deployments of pricing or recommendation engines.

Behaviors and Competencies

Analytical Thinking: Can apply critical thinking to analyse data, identify patterns, and make basic inferences.

Data Analysis: Can identify patterns and trends in data, propose hypotheses, and use statistical techniques to test them.

Data Literacy: Can identify relevant data sources, collect data, and use basic tools to interpret and report findings.

Critical Thinking: Can analyse and interpret data to inform decision-making, and propose solutions based on logical reasoning.

Attention to Detail: Can identify errors or inconsistencies in work and make necessary corrections.

Communication: Can effectively communicate complex ideas and information, and can adapt communication style to the audience.

Problem-Solving: Can identify problems, propose solutions, and take action to resolve them without explicit instructions.

Technical Expertise: Can apply technical knowledge and skills effectively in most situations, with occasional guidance.

Time Management: Can generally use time effectively and is working towards improving task prioritisation and deadline management.

Continuous Improvement: Can identify moderate areas for improvement and implement moderate changes.

Skill Level Requirements

2–4 years of experience in data science, machine learning, pricing analytics, or commercial operations.

Strong technical background in Python or R, SQL, and applied ML libraries (e.g. scikit-learn, TensorFlow, XGBoost).

Experience with data visualization tools (Tableau, Power BI, or similar).

Understanding of statistical methods, supervised/unsupervised learning, and business experimentation.

Ability to interpret and communicate insights clearly to non-technical stakeholders.

Comfortable working in a fast-paced, cross-functional environment with exposure to commercial decision-making.

Other Requirements

Exposure to pricing models, B2B enterprise sales, or SaaS/commercial optimization domains.

Familiarity with CRM (e.g., Salesforce) or Q2C systems.

Interest in finance, negotiation science, or behavioural economics.

The estimated annual pay range for this position is £45,000 - £60,000 which includes a base salary and bonus. The compensation for this position is dependent on job-related knowledge, skills, experience, and market location and, therefore, will vary from individual to individual.

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