Data Scientist

• Responsible and manages all aspects of the clinical trial data management process from study start up to post database lock for assigned projects.
• Oversee and/or perform database development and testing.
• Ensure that there is back-up, continuity, responsiveness, and that tasks are performed in a timely manner.
• Oversee project data entry process including development of data entry guidelines, training, data entry quality and resourcing.
• Ensure that clinical data management deadlines are met with quality.
• Develop CRF specifications from the clinical study protocol and coordinate the review/feedback from all stakeholders.
• Conduct database build UAT and maintain quality controlled database build documentation. Oversee overall quality of the clinical database.
• Responsible for creating, revising, appropriate versioning and maintaining data management documentation.
• Train clinical research personnel on the study specific CRF, EDC, and other project related items as needed.
• Perform line listing data review based on the guidance provided by the sponsor and/or Lead DM.
• Liaises with third-party vendors such as external data and EDC vendors in a project-manager capacity in support of timelines and data-related deliverables.
• May assist with SAS programming and quality control of SAS programs used in the Data Management department.

Senior Clinical Data Manager

• Primary Data Management (DM) contact (Lead DM role) for assigned clinical project(s) /program(s), ensuring that there is back-up, continuity, responsiveness, and that tasks are performed in a timely manner.
• Oversee project data entry process including development of data entry guidelines, training, data entry quality and resourcing.
• Provide input, assesses and manage timelines. Ensure that clinical data management deadlines are met with quality.
• Develop CRF specifications from the clinical study protocol and coordinate the review/feedback from all stakeholders.
• Conduct database build UAT and maintain quality controlled database build documentation. Oversee overall quality of the clinical database.
• Review and query clinical trial data according to the Data Management Plan
• Run patient and study level status and metric reporting
• Perform other duties as assigned

• Lead High-Impact AI Deployments
• Architect and deploy forecasting, optimization, clustering, and anomaly detection models
• Design and productionize Generative AI workflows
• Implement AI Agents that automate analysis and operational decisions
• Establish best practices for model monitoring, retraining, and governance
• Own the First “Aha” Moment
• Within 2–4 weeks, deliver a fully operational AI solution solving a core business pain point
• Design End-to-End Solutions
• Define data requirements and modeling strategies
• Guide data pipeline development in collaboration with Analytics Engineers
• Ensure scalability, performance, and robustness of deployed systems
• Act as a Strategic Advisor
• Translate AI capabilities into P&L impact
• Challenge client assumptions and refine problem framing
• Identify adjacent opportunities for optimization and AI expansion
• Influence executive-level decisions with data-driven insights
• Lead Client Adoption & Enablement
• Train technical and business teams on Arkham’s AI capabilities
• Establish feedback loops for continuous improvement
• Drive long-term AI maturity inside client organizations
• Elevate Arkham’s Implementation Standards
• Contribute reusable frameworks and modeling templates
• Improve internal best practices for ML deployment and GenAI integration
• Mentor junior Data Scientists and Analytics Engineers

Senior Forward Deployed Data Scientist

• Diseñar, desarrollar y desplegar modelos de aprendizaje automático (Machine Learning) y soluciones de IA para medir y optimizar la experiencia del cliente (CX)
• Implementar herramientas analíticas basadas en inteligencia artificial generativa y clásica para el análisis de productos y servicios financieros
• Interpretar grandes volúmenes de datos para identificar patrones de comportamiento y áreas de oportunidad en la interacción cliente-banco
• Colaborar activamente en la construcción de herramientas que permitan al Banco ser más ágil y preciso en la medición de sus servicios

Data Scientist Associate

• Extract, transform, aggregate, and analyze large and complex datasets using SQL, Python, R, Spark, and related technologies.
• Perform exploratory data analysis (EDA), feature engineering, and data validation to support analytics and modeling initiatives.
• Develop, evaluate, and maintain descriptive and predictive machine learning models using tools such as scikit-learn, Jupyter Notebooks, Python, R, and/or SAS.
• Apply statistical modeling and data mining techniques, including regression, classification, clustering, decision trees, and related methodologies.
• Utilize Generative AI and AI-assisted tools, including LLMs, coding assistants, and AutoML platforms, to improve analytical workflows and insight generation.
• Collaborate with stakeholders to translate business problems into analytical solutions and communicate findings effectively.
• Build and maintain business intelligence solutions and dashboards using Tableau, Power BI, or similar visualization platforms.

Associate Data Scientist

• Desarrollar y ejecutar modelos avanzados para la toma de decisiones crediticias y de negocio.
• Diseñar e implementar algoritmos de Machine Learning y modelos estadísticos para optimizar procesos internos.
• Realizar el seguimiento y monitoreo de los modelos de riesgo para asegurar su precisión y relevancia.
• Colaborar en el diseño de modelos de cobranza que mejoren la eficiencia operativa del área.
• Analizar grandes volúmenes de datos mediante herramientas de última generación y entornos de nube.

• Build and deploy demand forecasting and pipeline prediction models that project revenue outcomes at the campaign, segment, and market level
• Develop and maintain lead scoring, audience propensity, and opportunity scoring models to sharpen targeting and prioritize sales and marketing effort
• Analyze and model the relationship between marketing investment, channel performance, and revenue outcomes, connecting spend to growth
• Identify high-value market segments and audiences through data modeling to inform how and where we compete
• Develop personalization models and frameworks that enable tailored customer experiences across channels, from dynamic content and offers to next-best-action recommendations
• Own and shape the revenue data ecosystem, ensuring data integrity, influencing how data is captured, and making it model-ready across systems
• Collaborate with Marketing and Sales teams to define forecasting methodologies and embed models into operational workflows
• Translate complex model outputs into clear, actionable narratives that drive strategic decisions at the executive level

Marketing Data Scientist

• Collaborate closely with the Snowflake engineering team to define, spec, and validate data requirements for models and AI applications
• Serve as the translation layer between data engineering and AI/ML teams, ensuring data is structured, clean, and optimized for downstream use
• Enable and support agentic AI workflows by designing and maintaining the data layers that power agent memory, context retrieval, and tool-use
• Build and maintain data models and transformation logic on top of Snowflake to support predictive and generative AI use cases
• Identify and resolve data quality issues in collaboration with the engineering team
• Partner with marketing science and analytics teams to ensure data assets are accessible and model-ready
• Automate and streamline data validation processes to improve reliability of AI-facing data pipelines

Marketing Data Scientist – Data & AI Enablement

• Extract, transform, aggregate, and analyze large and complex datasets using SQL, Python, R, Spark, and related technologies.
• Perform exploratory data analysis (EDA), feature engineering, and data validation to support analytics and modeling initiatives.
• Develop, evaluate, and maintain descriptive and predictive machine learning models using tools such as scikit-learn, Jupyter Notebooks, Python, R, and/or SAS.
• Apply statistical modeling and data mining techniques, including regression, classification, clustering, decision trees, and related methodologies.
• Utilize Generative AI and AI-assisted tools, including LLMs, coding assistants, and AutoML platforms, to improve analytical workflows and insight generation.
• Apply Generative AI techniques such as prompt engineering, text summarization, classification, and LLM-assisted analysis in business or research applications.
• Collaborate with stakeholders to translate business problems into analytical solutions and communicate findings effectively.
• Build and maintain business intelligence solutions and dashboards using Tableau, Power BI, or similar visualization platforms.
• Define metrics, validate data quality, and support semantic layer development to ensure accurate reporting and business insights.
• Follow responsible AI principles, including awareness of data privacy, bias mitigation, ethical AI usage, and model limitations.

Associate Data Scientist, New College Grad

• Traduce las necesidades de negocio en problemas solucionables con datos.
• Extrae, depura y procesa información para generar análisis y modelos cuando aportan valor, y los convierte en recomendaciones accionables.
• Participa como team member de Advanced Analytics.
• Aterrizar los indicadores de éxito de las iniciativas del Proyecto Oasis.
• Generar y gestionar modelos predictivos y productivos para objetivos.

Científico de Datos

• Proactively reviews technical literature, learns a new methodology and aligns with Data Science leaders.
• Works with Data Science leaders to develop new innovative solutions to existing or new business challenges.
• Identifies business need, aligns with key stakeholders, organizes analytics team, seamlessly connects model findings to business strategy, and generates recommendations that fit business reality.
• Defines and implements models to uncover patterns and predictions creating business value and innovation.
• Leads complex cross-organizational project, which involves several Data Scientists.
• Provides guidance, training and mentoring to less experienced staff members.
• Develops clear presentations for senior decision makers including case for change, options, trade-offs and recommendation.
• Maintains proficiency within the data science domain by keeping up with technology and trend shifts.

Lead Data Scientist, Data Science

• En el rol de Científico de Datos Senior, buscar soluciones reproducibles para problemas complejos en un entorno empresarial.
• Colaborar en estrecha colaboración con un equipo de Ingeniería de ML.

Senior Data Scientist

• Develop and deploy data science and machine learning models
• Perform data preprocessing, transformation, and feature engineering
• Apply statistical analysis and exploratory data analysis techniques
• Optimize and tune models for performance
• Collaborate with teams to identify business requirements and develop data-driven solutions
• Conduct experiments and iterate on models to improve accuracy and efficiency
• Communicate technical findings and solutions to stakeholders

• Desarrollar y dar soporte a modelos de estimación de vida útil (RUL) utilizando datos de uso, degradación y ciclo de vida de componentes.
• Diseñar e implementar agentes de inteligencia artificial y workflows automatizados para casos de uso en ingeniería de confiabilidad.
• Construir, mantener y mejorar productos de datos empresariales, incluyendo dashboards de uso, confiabilidad e insights operativos.
• Trabajar con grandes volúmenes de datos complejos asegurando calidad, escalabilidad y confiabilidad de la información.
• Aplicar técnicas de machine learning (especialmente series de tiempo y modelos predictivos) para resolver problemas industriales.
• Colaborar con equipos multidisciplinarios de ingeniería y productos digitales para entregar soluciones listas para producción.
• Implementar soluciones en entornos distribuidos utilizando herramientas como PySpark y Databricks.
• Versionar y mantener código y modelos mediante plataformas como GitHub.

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