• Diseñar, desarrollar e implementar modelos de machine learning y deep learning para analítica predictiva y clasificación. • Desarrollar y ajustar soluciones de NLP, incluyendo trabajo con LLMs. • Realizar análisis de datos avanzados para la identificación de insights y soporte a decisiones estratégicas. • Escribir código eficiente y escalable en Python y PySpark. • Utilizar SQL para extracción, manipulación y consulta de grandes volúmenes de datos. • Construir y desplegar modelos mediante servicios de AWS (SageMaker, Glue, Lambda, entre otros). • Colaborar con equipos de data engineers, data analysts y stakeholders. • Mantenerse al día en tendencias y tecnologías de data science, IA y cloud computing.

Role Description Collaborate with cross-functional partners in operations, finance, marketing, and engineering to understand business imperatives and scope data science projects. - Explore possibilities to solve the business imperatives by identifying data sources, creating data where needed, and applying data science techniques. - Work with VP Data, VP of Analytics, and CFO to create data-informed business strategies and roadmaps. - Translate data needs into data product requirements, evaluate technologies, and identify opportunities to innovate and improve data science capabilities. - Use creative problem-solving skills to analyze data and build statistical/machine learning models to help solve business problems from different perspectives. - Work with data engineering lead to create services that can ingest and supply data to and from both internal and external sources and ensure data quality and timeliness. - Build the data science team and lead in data scientist recruitment process. - Guide and coach data scientists and own the deliverables of data visualization and dashboards to help the organization monitor performance, generate insights, and continuously improve user experience. - Continuously grow the data science team’s skillsets. - Establish playbooks to drive data product development process and consistent outcomes. - Use strong communication skills (written and verbal) to lead the full lifecycle of model development, which spans from business problem discovery, data discovery to model deployment and monitoring. - Evangelize data science across the entire company; identify and make the business case for data science based use cases. - Where needed, conduct hands-on wrangling, processing, cleansing, and verifying data from different sources used for analysis. - Analyze data with complex relationships, hierarchical levels, and time series, such as Pandas, PySpark. - Use and recommend a wide range of machine learning algorithms and statistical modeling, including latest LLM developments. - Utilize data visualization and dashboard tools, such as Tableau, PowerBI, and Snowflake. - Up to 10% domestic travel required for meetings/client visits; 100% remote; must reside in U.S.; reports to HQ in New York, NY. - Salary: $336,350 - $356,350 per year. Qualifications - Bachelor’s Degree or U.S. equivalent in Quantitative Methods, Statistics, Econometrics, or related field. - 5 years of professional experience as a Data Scientist, Operations Research Analyst, or any occupation, job title, or position leading a data science team in a technology organization. Requirements - 5 years of professional experience implementing data science solutions by identifying relevant data sources, creating data pipelines, and applying advanced data science techniques to support business decision-making. - 5 years of professional experience designing and developing machine learning models and statistical algorithms to analyze structured and unstructured data from both internal and external sources. - 5 years of professional experience collaborating with cross-functional teams to gather business requirements, define technical scopes, and deliver actionable data insights. - 5 years of professional experience utilizing tools and libraries including Python, SQL, Spark, PyTorch, Pandas, and PySpark to perform data wrangling, cleansing, and advanced analytics. - 5 years of professional experience developing and maintaining data visualization dashboards using tools including Tableau to monitor performance and communicate insights to stakeholders. - 5 years of professional experience establishing scalable data product development processes through consistent use of playbooks, code standards, and reproducible pipelines. - 5 years of professional experience leading the full lifecycle of machine learning models from business problem discovery to model deployment and performance monitoring. - 5 years of professional experience evangelizing data science practices across organizations, including coaching team members, supporting recruitment efforts, and promoting a data-driven culture. Contact Apply online at fanaticsinc.com/careers or email resume to: WorkWithFanatics@FanaticsInc.com . Must specify Ad Code ZCKM in subject line.

• Review, edit, and draft a variety of legal agreements, including vendor, partnership, and independent contractor contracts. • Manage the IronClad contract lifecycle management platform, including contract workflows, status tracking, and system maintenance. • Oversee the Independent Contractor review process and support ongoing compliance with organizational policies. • Create and maintain contract templates, guidance documents, FAQs, playbooks, and other resources that support consistent contract practices. • Develop and deliver resources that increase organization-wide understanding of the Contracts Policy and legal processes. • Maintain Legal Affairs content on TFAHub to ensure materials remain current, accessible, and useful. • Produce monthly and quarterly reports on legal matters, contract volume, and team activity. • Analyze trends and recommend improvements to legal operations, workflows, and user experience.

• Define, execute, and lead the data science strategy in alignment with the company’s vision, goals, and objectives—establishing initiatives that drive premium growth, improve retention, enhance customer satisfaction, and maximize shareholder value. • Provide direction and guidance to a high-performing data science team; recruit, mentor, and develop data scientists and machine learning engineers while fostering a culture of innovation, continuous improvement, and technical excellence. • Embed AI-first thinking and hands-on AI tool adoption as a standard across the team, ensuring the organization remains at the forefront of applied machine learning and LLM-based capabilities. • Partner with senior executives, insurance business leaders, and cross-functional teams to define project scope, integrate data science across the organization, and deliver measurable results. • Manage the data science budget, establish project timelines, and define KPIs and metrics to assess the impact and effectiveness of data science initiatives. • Dive into operational data to identify opportunities to improve fidelity, accuracy, and accessibility; design and implement A/B tests and experiments to measure the impact of models and business tactics. • Own the data science technical roadmap and heavily influence data engineering and analytics roadmaps through regular collaboration with engineering and business stakeholders.