Data Science & Value Creation Consultant

About Valitas Valitas helps private company shareholders unlock their full value potential, targeting $1B in client value by 2030 and at least $100M per client. We work with a select number of clients, applying our Shareholder Value Architect system to drive focused, disciplined execution backed by proprietary value creation tools and a curated network of high-calibre experts. Job overview Valitas is seeking a Data Science & Value Creation Consultant to support project-based engagements as a remote contractor. The role focuses on analyzing client datasets to identify opportunities to improve profitability, revenue, margins, and operational performance. The successful candidate will be able to uncover non-obvious insights, quantify potential business impact, and communicate findings clearly to support decision-making. The emphasis is on deep analytical discovery and value creation rather than reporting. Responsibilities - Analyze operational, financial, customer, and commercial datasets, which are often large and fragmented. - Identify opportunities to improve EBITDA, margins, revenue growth, customer retention, and operational performance. - Assess pricing, customer profitability, commercial performance, and other key business drivers. - Investigate trends, performance gaps, and underlying drivers across large datasets. - Quantify the potential impact of identified opportunities. - Present findings and recommendations in a format that can be used by management teams and decision-makers. - Work alongside the Valitas team on value creation and performance improvement initiatives across multiple engagements. Required Capabilities Business & Analytical - Strong analytical and business problem-solving skills. - Ability to connect analytical findings to EBITDA and ROI outcomes and quantify the financial impact of identified opportunities. - Experience identifying performance drivers and improvement opportunities through data. Technical - Strong experience with Python, SQL, or comparable analytical tools. - Practical experience applying methods such as regression analysis, clustering, segmentation, and statistical analysis. - Experience working with large, complex, and fragmented datasets. Communication - Ability to communicate analytical findings to non-technical audiences. - Experience presenting recommendations to business leaders, operators, investors, or management teams. Preferred Background Preference will be given to candidates with experience in: - Private equity portfolio analytics - Value creation teams - Analytics-heavy operational, strategic, or consulting roles Candidates should be able to demonstrate examples of analysis that led to measurable operational or financial improvements.