• Analyze large and complex datasets to identify trends, patterns, risks, and business opportunities • Apply statistical, mathematical, and quantitative techniques to solve medium-scale business problems • Build descriptive and explanatory models or leverage existing predictive models to support business strategies • Conduct exploratory data analysis (EDA), hypothesis testing, and data validation exercises • Develop actionable insights and recommendations that support operational efficiency, growth, and profitability • Collaborate with cross-functional teams including IT, Data Engineering, Data Science, and business stakeholders • Collect, integrate, and transform data from multiple sources into cohesive datasets for analysis • Assess data quality and apply data hygiene techniques to ensure accuracy, reliability, and consistency • Support monitoring and deployment of predictive models into business workflows • Create dashboards, reports, and visualizations using tools such as Power BI and Tableau • Communicate analytical findings and recommendations to business partners and leadership in a clear and concise manner • Use storytelling and data visualization techniques to simplify complex information for decision-making • Identify and troubleshoot data quality issues in partnership with technical teams • Stay informed on emerging analytics, decision science, and machine learning methodologies and best practices • Participate in continuous learning and professional development opportunities to enhance technical and business expertise

Role Description As a Senior Data Scientist for the Airvoyant POD, you will serve as the technical backbone for our next-generation, data-driven aviation software. You will anchor the design, development, and deployment of advanced machine learning models, predictive analytics, and production-grade data pipelines that transform aircraft maintenance and operational performance for global airlines, MROs, and aviation vendors. This role is critical to converting highly complex, structured, and unstructured datasets into scalable solutions and intelligent automation. Working cross-functionally with product, engineering, and business leaders, your predictive insights will directly optimize platform innovation, accelerate operational efficiency, and deliver measurable business impact for our external clients. Key Responsibilities - Own the end-to-end design, development, validation, and production-grade deployment of predictive statistical and machine learning models. - Lead the extraction and interpretation of complex datasets to build scalable data products, custom APIs, and decision-automation algorithms. - Optimize model performance, engineering robust frameworks to monitor data integrity, model drift, and long-term system reliability. - Design and execute rigorous experiments, including A/B testing and hypothesis testing, to deliver clear, data-driven recommendations to senior leadership. - Collaborate with cross-functional engineering and product teams to integrate advanced analytics into customer-facing products and democratize insights through interactive visualizations. Qualifications - 5–7+ years of professional experience in data science or advanced analytics, with a proven track record of delivering production-grade data solutions. - Advanced proficiency in Python for machine learning and data analysis (pandas, NumPy, scikit-learn, XGBoost). - Expert-level SQL skills for complex data querying, transformation, and performance optimization. - Strong experience with cloud platforms (AWS, Azure, or GCP) alongside big data technologies (Spark, Hadoop, or Presto). - Hands-on experience with containerization (Docker) and modern data pipeline orchestration. - Ownership mindset with exceptional communication skills to translate complex analytical concepts into actionable business strategies. - Advanced English (written and spoken). Nice to Have - Direct experience in aviation, MRO, ERP, or operations-heavy industries. - Familiarity with time-series analysis, predictive maintenance, or reliability modeling. - Strong exposure to MLOps practices, including model versioning, lifecycle management, and real-time streaming analytics. - Hands-on experience with Amazon Redshift for large-scale data warehousing and Amazon QuickSight for business intelligence. Benefits - 100% Remote - Holidays off - Paid Time Off - Health insurance assistance - Competitive USD compensation - Growth opportunities

• Build machine learning and AI solutions that support pricing, forecasting, and revenue management decisions in hospitality. • Analyze large, sometimes messy datasets to identify patterns, risks, and opportunities, and turn them into practical recommendations or product capability. • Write clean Python and SQL, working with data pipelines and analytical workflows to prepare, validate, and operationalize data. • Apply modern AI techniques, including LLMs and AI coding tools such as Claude Code, thoughtfully to accelerate prototyping, learning, and development while maintaining technical quality. • Partner closely with product, engineering, and cross-functional stakeholders to translate business problems into analytical solutions and communicate insights clearly.

• Own ongoing data entry in specified systems as demanded by the business • Enforce our data governance guidelines and suggest new processes and controls to improve our consistency • Develop an in-depth understanding of our systems and operate as an SME. This person will represent Master Data cross-functionally and will assist with onboarding and training new team members • Break down complex, multi-step projects into clear tasks that other associates can easily execute • Track project progress and communicate status updates to the Director and relevant stakeholders • Scope new projects as they come in, identifying what Master Data owns, what Master Data contributes to, and what tasks are owned by other teams • Support the creation of new processes that impact Master Data, even when Master Data is not the ultimate owner of that process • Represent the Director and the Master Data team in meetings when appropriate • Own the team’s short-term and long-term project tracking. This includes building tools that allow the team to measure progress as work is completed • Partner with Retail, Operations, Accounting, Finance, and IT to maintain master data including creation, modification, cleansing, and enrichment • Own quarterly audits of data integrity and accuracy • Support data integration initiatives in partnership with the M&A team • Partner with internal and external audit teams to document procedures, respond to follow-up questions, and assist with ongoing SOX documentation