Role Description We are seeking a highly analytical and detail-oriented Senior Analyst to support media analytics and measurement across campaigns. This role is responsible for transforming data into clear, actionable insights that inform campaign optimization and support future planning. The Senior Analyst partners with media, strategy, and analytics teams to ensure data is accurate, insights are relevant, and outputs are polished and decision-ready—with a focus on enabling faster, smarter media decisions. Responsibilities - Analyze cross-channel media performance and deliver insights to support campaign optimization and planning - Translate data into clear findings and actionable recommendations - Support development and execution of measurement frameworks and learning agendas - Conduct audience, competitive, and performance analysis to inform strategy - Partner with media teams to identify in-flight optimization opportunities - Build and maintain dashboards and reports that are intuitive and decision-ready - Support data automation and pipeline development (e.g., GCP, Funnel.io) - Ensure data accuracy, consistency, and quality across outputs - Contribute to presentations with data-driven storytelling, not just data reporting Core Capabilities (Strategy, Analytics & Tools) - Applies measurement frameworks to analyze media performance and audience behavior - Identifies performance drivers, trends, and optimization opportunities - Synthesizes data across sources into clear, actionable insights - Uses tools (CM360, Meta, Adobe/GA4, Tableau/Looker, Funnel.io) to analyze and visualize data effectively - Supports automation efforts to reduce manual reporting and improve efficiency - Applies analytical thinking to answer business and campaign performance questions - Maintains strong data quality, organization, and taxonomy standards Communication & Collaboration - Translates data into clear, concise insights and recommendations - Contributes to client and internal discussions with fact-based, insight-driven input - Supports development of polished presentations and reporting outputs - Partners with cross-functional teams to ensure alignment on performance and insights - Continuously improves deliverables to move from reporting toward insight-driven storytelling Qualifications - 3–5+ years of experience in media analytics, marketing analytics, or data analysis - Strong understanding of paid media metrics, performance drivers, and campaign structure - Experience translating data into insights and actionable recommendations - Familiarity with measurement frameworks and performance analysis approaches - Proficiency in analytics and visualization tools (e.g., Adobe Analytics, GA4, Tableau, Looker) - Experience with data tools or automation (e.g., Excel, SQL, Funnel.io, or similar) - Strong attention to detail and ability to deliver accurate, high-quality outputs - Effective communication skills with ability to present insights clearly and concisely Benefits - Paid Family Care for parents and caregivers for 12 weeks or more - Monetary assistance and support for Adoption, Surrogacy and Fertility - Monetary assistance and support for pet adoption - Employee Assistance Programs and Health/Wellness/Comfort reimbursements - Tuition Assistance - Paid time off that includes Flexible Time off, Vacation, Annual Sick Days, Volunteer Days, Holiday and Identity days, and more - Matching Gifts programs - Flexible working arrangements - ‘Work Your World’ Program encouraging employees to work from anywhere - Business Resource Groups that support multiple affinities and alliances

Role Description As a Sr. Data Science Engineer Spec-Data Scientist, you'll be part of the internal Information Technology team that keeps CTC's business operations running efficiently. Supporting employees across the organization, you'll contribute to a collaborative, customer-focused environment where technology enables engineers, researchers, and business professionals to accomplish their mission. - Collaborate with business leaders to identify high-impact business problems and translate them into data science and data analysis projects. - Collect, process, and analyze complex datasets from various sources to identify trends, patterns, and insights that inform business strategy. - Develop and implement predictive models and machine learning algorithms to solve business problems, such as forecasting, customer segmentation, and optimization. - Develop and maintain detailed, compelling dashboards and reports for both technical and non-technical audiences using business intelligence (BI) tools. - Perform exploratory data analysis (EDA) and statistical analysis to uncover patterns, trends, and anomalies. - Create clear, compelling, and actionable data visualizations and reports to communicate complex findings to both technical and non-technical audiences. - Design and execute A/B tests and other experiments to measure the impact of different initiatives. - Work with data engineers to build and improve data pipelines, ensuring data quality and accessibility. Qualifications - Bachelor's or Master's degree in a quantitative field such as Statistics, Mathematics, Computer Science, or Economics. - 3–5 years of hands-on experience in a Data Scientist or Senior Data Analyst role. - Strong proficiency in Python (including libraries like Pandas, NumPy, and Scikit-learn), SQL, and PySpark. - Solid understanding of statistical analysis, data mining techniques, and machine learning algorithms (e.g., classification, regression, clustering, and decision trees). - Excellent verbal and written communication skills with the ability to tell a story with data. - Proven ability to approach complex problems with a structured, analytical, and inquisitive mindset. Requirements - Experience with cloud-based data platforms (e.g., AWS, Azure, GCP). - Experience with big data technologies (e.g., Spark, Hadoop). - Experience with data visualization and analysis tools (e.g., Tableau, Power BI, Matplotlib, R, SAS). - Experience with natural language processing (NLP) or other forms of text analysis. - Knowledge of experimental design and causal inference techniques. - Experience in research & development, government contracting, and/or highly regulated industry domains. Benefits - Competitive salary and benefits package. - Recognition for quality work. - Exceptional career growth and educational opportunities. - Work-life balance is valued and supported.

• Partner with Product & Engineering teams to analyze and optimize product engagement and the full customer funnel, including acquisition, conversion, retention, and churn. • Build and maintain statistical models and analyses related to key product use cases such as A/B tests, cohort analysis, and conversion/retention optimization. • Design, analyze, and interpret experiments (A/B and multivariate tests) across marketing channels and on-site experiences, ensuring statistical rigor and clear recommendations. • Translate ambiguous business questions into well-scoped analytical projects with clear success metrics and timelines. • Collaborate with data engineering and analytics engineering partners to define data requirements and ensure reliable, well-modeled datasets for marketing analytics. • Clearly communicate findings and recommendations to non-technical stakeholders through presentations, dashboards, and written documentation. • Contribute to shared analytics and modeling best practices within the data science team, including code quality, validation, and documentation. • Support junior team members through informal mentorship, code review, and knowledge sharing where appropriate. • Stay current on industry trends and techniques in marketing analytics, experimentation, and applied data science, and apply them pragmatically to Hone’s context.

Role Description We’re seeking a Data Scientist with hands-on experience in machine learning engineering and MLOps. In this role, you’ll own the full model lifecycle — from research and experimentation through deployment, monitoring, and iteration. You’ll work within our AWS SageMaker Studio environment and collaborate closely with engineering, operations, and product teams to deliver models that drive measurable business outcomes. Key Responsibilities - Develop, train, and evaluate machine learning models to solve business problems across operations, legal services, and marketing. - Own the full ML lifecycle: data preparation, feature engineering, model training, validation, deployment, and monitoring. - Build and maintain MLOps pipelines using AWS SageMaker Studio, including experiment tracking, model registry, and automated retraining workflows. - Partner with product and operations teams to translate business requirements into data science solutions. - Monitor deployed models in production, identify performance degradation, and drive continuous improvement. - Document methodologies, model performance benchmarks, and technical decisions for internal knowledge sharing. - Stay current with advances in ML and data science tooling, and advocate for best practices across the team. Qualifications - 3+ years of experience in data science or a closely related role, with demonstrated ML engineering and MLOps responsibilities. - Strong proficiency in Python for data science and ML development (pandas, scikit-learn, PyTorch or TensorFlow). - Hands-on experience with AWS SageMaker Studio for model development, training, and deployment. - Solid understanding of MLOps principles: model versioning, pipeline automation, drift detection, and production monitoring. - Experience with SQL and working with structured data in cloud data warehouses or relational databases. - Proven ability to translate complex data science findings into clear, actionable insights for non-technical stakeholders. - Strong self-direction and communication skills suited for a remote work environment. Requirements - Experience in the legal, collections, or financial services industry. - Background in targeted mail marketing, direct mail modeling, or customer segmentation. - Familiarity with AI coding agents and agentic development workflows (e.g., Claude, Copilot, Cursor, or similar tools). - Experience with propensity modeling, uplift modeling, or response prediction. - Exposure to LLM-based workflows or applied NLP in a production setting. - Data engineering experience with modern tooling such as Dagster, Airbyte, and dbt. - Familiarity with AWS data services including Glue, Lambda, Redshift, and Step Functions. Benefits - Fully remote position with flexible working hours. - Comprehensive health, dental, and vision insurance. - 401(k) with company match. - Paid time off and company holidays. - Opportunity to shape data science strategy at a growing, industry-leading company. Salary Range $110,000-$130,000 depending on experience.