• Partner directly with business stakeholders to identify opportunities where data and machine learning can improve decisions, efficiency, or outcomes • Design experiments and hypotheses that can be validated quickly using available data and pragmatic modeling approaches • Own models end-to-end—from data preparation and feature engineering through deployment, monitoring, and iteration based on real-world results • Deploy ML models into production using AWS-native tooling and integrate them into operational workflows and downstream systems • Implement ML training and inference pipelines on Amazon SageMaker, including pipelines, endpoints, model registry, and monitoring • Monitor model performance (accuracy, drift, stability, business KPIs) and iterate based on real-world impact • Build and operate data ingestion and transformation pipelines across batch and event-driven workloads using AWS Glue, zero‑ETL integrations, Step Functions, EventBridge, and related services • Collaborate closely with IT, Security, and Platform Engineering teams to align with enterprise security, compliance, and operational standards • Use infrastructure as code (Terraform, CDK, or CloudFormation) to create repeatable, scalable environments • Own and operate S3-based data lake infrastructure, including Iceberg table formats, AWS Glue Data Catalog, and AWS Lake Formation • Implement and enforce data zone architecture (e.g., raw, curated, and consumption zones) to support governed data access and lifecycle management • Define and apply data access controls using Lake Formation permissions and IAM-aligned policies • Establish and maintain data governance practices, including schema management, schema evolution, and lineage tracking • Ensure data assets are discoverable, auditable, and secure through cataloging, metadata management, and access controls • Build end-to-end observability using CloudWatch, Datadog, pipeline SLAs, data quality checks, and model drift detection • Establish operational runbooks and support procedures for governed data and ML platforms • Apply cost-aware design when selecting data processing, training, and inference approaches • Optimize Glue, SageMaker, and storage usage to deliver value efficiently at scale • Continuously improve platform reliability, scalability, and cost efficiency as data and ML workloads grow

About Hypha Metrics Hypha Metrics is redefining how media data is measured, connected, and understood. Our platform provides the foundational infrastructure that powers audience insights for the modern media ecosystem. We help clients make smarter decisions by transforming fragmented data into unified, actionable intelligence. Role summary Design and deliver rigorous, scalable measurement solutions that uncover true media impact and drive media investment decisions. This role combines causal inference, experimentation, statistical modeling, and production data engineering to measure incrementality, optimize media mix and inform campaign strategy across digital and offline channels. Key responsibilities - Design and analyze randomized and quasi-experimental tests (holdouts, geo-tests, RCTs) to measure advertising incrementality and lift. - Build and maintain causal models (difference-in-differences, synthetic controls, hierarchical Bayesian, uplift modeling) and marketing mix models (MMM) for multi-channel attribution. - Develop and productionize scalable end-to-end pipelines for event-level ad exposure, conversions, and offline-sales ingestion (ETL/ELT, validation, monitoring). - Work with data engineering to keep measurement datasets clean, deduplicated (identity resolution), and privacy-compliant. - Own feature engineering, model training, validation, and deployment in Python/R and cloud environments (BigQuery, Snowflake, Dataproc/AWS/GCP). - Produce clear, actionable dashboards and executive-ready insights for product, media, and client teams; present findings to stakeholders. - Implement automated reporting and CI/CD for model retraining and performance monitoring; establish measurement governance and documentation. - Stay current on the ad-tech/measurement ecosystem (ATtribution frameworks, walled gardens, ID solutions) and recommend measurement strategy changes. Required qualifications - 3+ years experience building statistical or ML models in ad-tech, marketing analytics, agency measurement, or a related data science role. - Strong statistics/causal inference fundamentals: experimental design, hypothesis testing, regression, hierarchical models. - Proficient in Python (pandas, scikit-learn, PyMC/Stan or equivalent) and SQL; experience with R is a plus. - Hands-on experience with event-level ad/exposure and conversion data, logs from ad servers/DSPs, or retail/point-of-sale integration. - Experience working with cloud data warehouses (BigQuery, Snowflake, Redshift) and ETL tooling (Airflow, dbt, Kafka). - Excellent written and verbal communication; proven ability to translate technical results to non-technical stakeholders. Preferred qualifications - Experience with incrementality platforms or approaches (e.g., Measured, experimentation platforms, proprietary lift frameworks). - Familiarity with marketing mix modeling (time-series, regularized regression, Bayesian MMM). - Experience with causal ML / uplift modeling and Bayesian inference tools (PyMC3/4, Stan). - Knowledge of identity resolution, privacy-preserving measurement (privacy regulation awareness, cohort-based measurement, differential privacy concepts). - Experience deploying models to production (Docker, CI/CD, MLOps patterns) and instrumenting model monitoring. - Background working with brand/performance media, cross-channel measurement, and agency/client workflows. KPIs / success metrics - Number of incrementality tests designed, executed, and turned into action. - Accuracy and explainability of models (e.g., holdout prediction error, calibration). - Time-to-insight (from data ingestion to stakeholder-ready report). - Business impact (measured media savings or ROI improvements attributable to recommendations). - Adoption rate of measurement outputs by media planners/clients. Nice-to-have (company fit & soft skills) - Proven consultative experience with clients or internal stakeholders. - Comfort operating in ambiguous environments and balancing speed vs. statistical rigor. - Ability to mentor junior data scientists and evangelize measurement best practices across teams.

• Supervise and support participants throughout the practice-oriented modules during the entire duration of the training • Deliver instruction in the online classroom for the “Certified Data Scientist” program • Provide assistance with technical questions and offer guidance to ensure learning success • Give feedback on learning progress, formulate constructive feedback, and actively foster the learning process • Contribute your own ideas and subject-matter expertise to continuously optimize the training concept

ICF seeks a On-call Senior Environmental Scientist to provide technical and training support for our U.S. Environmental Protection Agency (EPA) projects with the Office of Superfund Remediation and Technology Innovation (OSRTI), Office of Brownfields and Land Revitalization (OBLR), and Office of Resource Conservation and Recovery (ORCR). What you’ll do: - Leverage hands-on knowledge and experience with site characterization and remediation technologies to promote improvements to the cleanup of contaminated sites. - Apply extensive knowledge of CERCLA and RCRA regulations and policies by identifying and evaluating ways to advance contaminated site cleanup including development of conceptual site models, site strategies, optimization recommendations, cost estimates and advising on use of innovative technologies and best practices. - Support improvements to EPA’s environmental data management and analyses through development and use of leading-edge technology applications. - Manage multi-disciplinary project teams and client engagement. What you’ll need: - Bachelor’s or master’s degree in geology, hydrogeology, environmental science, engineering, or related technical field. - 10+ years of experience conducting site characterization and remediation of contaminated sites. - Extensive knowledge of EPA cleanup programs and policies, particularly CERCLA and RCRA. - Expertise in leading and developing technical deliverables and training materials. Professional skills: - Team player with demonstrated project management skills including the ability to work in a fast-paced environment. - Demonstrated ability to effectively engage with clients. - Excellent written and oral communications skills. Working at ICF ICF is a global advisory and technology services provider, but we’re not your typical consultants. We combine unmatched expertise with cutting-edge technology to help clients solve their most complex challenges, navigate change, and shape the future. We can only solve the world's toughest challenges by building a workplace that allows everyone to thrive. We are an equal opportunity employer. Together, our employees are empowered to share their expertise and collaborate with others to achieve personal and professional goals. For more information, please read our EEO policy. We will consider for employment qualified applicants with arrest and conviction records. Reasonable Accommodations are available, including, but not limited to, for disabled veterans, individuals with disabilities, and individuals with sincerely held religious beliefs, in all phases of the application and employment process. To request an accommodation, please email Candidateaccommodation@icf.com and we will be happy to assist. All information you provide will be kept confidential and will be used only to the extent required to provide needed reasonable accommodations.  Read more about workplace discrimination rights or our benefit offerings which are included in the Transparency in (Benefits) Coverage Act. Candidate AI Usage Policy At ICF, we are committed to ensuring a fair interview process for all candidates based on their own skills and knowledge. As part of this commitment, the use of artificial intelligence (AI) tools to generate or assist with responses during interviews (whether in-person or virtual) is not permitted. This policy is in place to maintain the integrity and authenticity of the interview process.  However, we understand that some candidates may require accommodation that involves the use of AI. If such an accommodation is needed, candidates are instructed to contact us in advance at candidateaccommodation@icf.com. We are dedicated to providing the necessary support to ensure that all candidates have an equal opportunity to succeed.   Pay Range - There are multiple factors that are considered in determining final pay for a position, including, but not limited to, relevant work experience, skills, certifications and competencies that align to the specified role, geographic location, education and certifications as well as contract provisions regarding labor categories that are specific to the position. The pay range for this position based on full-time employment is: $81,499.00 - $152,404.00 Nationwide Remote Office (US99)