• Partner with other data scientists, data engineers, and business stakeholders to generate actionable insights. • Build and maintain tools, dashboards, and insights for Sales. • Design and implement rigorous statistical frameworks to evaluate incrementality. • Translate ambiguous business problems into structured analytical approaches. • Write production-quality code (Python, SQL, etc.) to manipulate large-scale datasets. • Communicate clear, data-driven recommendations.

Role Description We're looking for a Capital Raiser to join our investor development team. - Identify, qualify, and build relationships with institutional capital sources (family offices, PE funds) for our clients. - Conduct market intelligence. - Manage investor outreach. - Coordinate meetings. - Provide strategic feedback on investor appetite and deal structure preferences. - Report directly to the Managing Partner. Qualifications - 5+ years in venture capital, private equity, investment banking, or institutional fundraising. - Proven success building relationships with institutional investors. - Strong network in the capital ecosystem. - Real estate, B2B tech, or cleantech experience a plus. - Valid FINRA license (Series 7, 63, 65, or 79). If unlicensed, you must be willing to obtain licensure within 90 days. We'll sponsor the process internally. - Clean compliance background. Benefits - Success-fee based compensation. You earn a percentage of fees EPL generates from clients on which you provide material investor development services. - Payments occur upon capital close. No salary component. - Direct access to established investor relationships. - Senior-level engagement (report to Managing Partner, no bureaucracy). - Meaningful upside from multiple successful closures. - Boutique agility in a high-quality client base. - Work across growing sectors. Not for you if: - You need salary stability or can't operate with variable compensation tied to outcomes. How to Apply: Submit your resume, a brief cover letter, and your FINRA license status to: Company Description At EPL Group, we are a distinguished family office that specializes in making direct investments in rapidly expanding enterprises led by visionary founders and family proprietors. What sets us apart as an investment collaborator is our exceptional approach, which distinguishes us in the industry. We firmly believe that nurturing exceptional companies is, fundamentally, a human endeavor.

Role Description The Data Scientist will be part of the Research & Development team, responsible for designing, building and maintaining internal systems used by management, coaches, scouts, analysts, and all other Hockey Operations staff. The primary focus of this role will be creating and communicating knowledge about hockey players, teams, systems, strategies, development, and training using data, often via long-term research projects. This position is available for in-person or remote work (with occasional travel). - Quantify, evaluate, and project the performance and abilities of hockey players and teams - Analyze data from all aspects of hockey operations - Build, validate, and automate models for hockey player and team performance - Collaborate with other members of the R&D department to address and support all existing data-related requests from across hockey operations - Create, summarize, and deliver statistical reports to stakeholders across hockey operations - Communicate effectively and directly with hockey operations staff - Perform all job duties in a variety of work settings including virtual meetings and distributed work environments, at in-person meetings, via phone/email, and in other settings - Clearly communicate complex technical concepts to others - Creatively use existing data to advance our understanding of the sport of hockey, the performance of players and teams, etc. - Perform ad-hoc and time-sensitive data analyses upon request - Maintain the utmost confidentiality - Perform other related duties as assigned Qualifications - Bachelor’s degree or higher in statistics, data science, machine learning, computer science, mathematics, engineering, or a related field - Hockey and/or sports analytics experience is highly preferred - Demonstrated experience working with player/object tracking data (ex. via computer vision or RFID systems) and/or skeletal tracking data (ex. via pose estimation) is preferred - Strong knowledge of programming for data analysis, preferably using R and/or Python - Demonstrated experience working with large, complex datasets - Experience building a variety of statistical models, and an understanding of those models - Experience writing shared code and using version control tools in team work environments - Ability to responsibly analyze and handle sensitive and confidential data - Ability to work a flexible schedule (including nights, weekends and holidays when necessary) and to occasionally travel Requirements - Knowledge of and Applied Experience in Statistics, Machine Learning, Data Science, etc.: - Experience working on long-term, open-ended, data-driven research problems (preferably in sports analytics) - Understanding of (and experience building) a variety of statistical models (e.g. generalized linear models, generalized additive models, hierarchical models, mixture models, non-parametric models, tree-based models, Bayesian models, boosting, unsupervised learning, neural networks, time series models, etc.) - Understanding of modern techniques for analyzing spatio-temporal data, such as recurrent neural networks, convolutional neural networks, transformers, etc. - Hockey and/or Sports Analytics Experience: - Deep knowledge of hockey and/or sports analytics - Strong performance in public data analysis competitions - Demonstrated experience working with player/object tracking data (e.g. via computer vision or RFID systems) and/or skeletal tracking data (e.g. via pose estimation) - Critical Competencies: - Strong attention to detail and highly organized - Ability to manage multiple projects at once - Excellent written and verbal communication skills - Flexibility to adapt to changing priorities - Collaborative and creative - Strong problem-solving skills Compensation This role will pay between $62,500 - $75,000 annually. Note: Wages will be determined based on factors such as candidate experience, qualifications, skill set and internal equity. Benefits - Medical Plans: Comprehensive and affordable medical plans; supplemental vision care, dental care, life insurance coverage, fully paid, short and long-term disability and life insurance coverage. - Investments: 401(k) with employer matching; annual discretionary defined employer contributions - Paid Time Off: Paid Volunteer Days - Sabres Experience: (up to 4) Comp tickets to Buffalo Sabres, Buffalo Bandits, Rochester Americans when available - Wellness: Annual Health & Wellness fair, monthly wellness initiatives including but not limited to lunch & learns, fitness classes and challenges. - Sabres Family: many annual social events for employees and their families to enjoy, including various holiday parties and activities. DEI committee with annual initiatives, ERG (Employee Resource Groups), Community Volunteer opportunities and more.

• Construção de soluções de IA para o time comercial • Desenvolver ferramentas e automações baseadas em IA para: • Geração e qualificação de leads • Suporte ao processo de vendas (roteiros, follow-ups, propostas) • Priorização de oportunidades • Integrar soluções com CRM e ferramentas existentes • Desenvolvimento de agentes de IA • Criar e evoluir agentes autônomos (ex: SDRs virtuais) • Automatizar etapas do funil comercial com IA • Testar, iterar e escalar agentes com base em performance real • Aumento de produtividade da operação • Identificar gargalos operacionais nos canais: • Franquias • Consultoria • Inside Sales • Implementar soluções que reduzam esforço manual e aumentem eficiência • Parceria com áreas de negócio • Trabalhar próximo aos líderes comerciais para entender necessidades reais • Traduzir problemas de negócio em soluções técnicas viáveis • Atuar como ponte entre tecnologia e operação • Gestão de performance e melhoria contínua • Definir métricas de sucesso (ex: conversão, tempo de ciclo, produtividade) • Rodar testes e experimentos contínuos • Escalar soluções com impacto comprovado