• Management of data management vendors providing consistent team direction/guidance and monitoring quality of work through vendor audits and key performance metrics. • Development and maintenance of program level DM deliverable timelines in accordance with program development strategy. Actively drives internal and external timeline negotiations, as needed. • Represent data management at cross-functional team meetings providing accurate study status updates and proactive communication/escalation of data management issues and risks. • Facilitate cross-functional sponsor review meetings of eCRF design and edit check specifications. • Facilitate and perform cross-functional sponsor EDC user acceptance testing. • Facilitate the development of data transfer agreements with central/specialty labs and other external data vendors. Ensuring data quality from data capture to reporting. • Facilitate the development and implementation of key data and metrics reports/listings. • Perform sponsor review of DM essential documents including annotated CRFs, eCRF Completion Guidelines, Data Management Plans and DB Go Live and Lock documentation.

Role Description Als Datenwissenschaftler:in in unserem zentralen Data Science Team lebst Du Deine Passion und bestätigst mit Deinen Modellen unseren Führungsanspruch im Bereich Fashion & Lifestyle als echter internationaler Omnichannel Department Store. - Du entwickelst digitale Produkte (weiter), treibst Analysen und befeuerst nachhaltig unser Verhältnis zum Kunden. - Du arbeitest bei der Entwicklung von Geschäftsprozessen aktiv mit und bist bereit Data Science Tools mit unseren Partnern zu konzipieren. - Du begleitest Deine Produkte von der Ideenentwicklung bis zur Implementierung. - Du beteiligst Dich am fachlichen Austausch zwischen allen Datenwissenschaftler:innen bei Breuninger. Qualifications - Du hast ein Studium in einem zahlen- und datenaffinen Fachbereich abgeschlossen, zum Beispiel Mathematik, Data Science, Informatik, Wirtschaftsinformatik oder einem vergleichbaren Studiengang. - Du bringst fundierte Kenntnisse in Python für die Datenanalyse mit und kannst auch komplexe Datenbankabfragen mit SQL sicher gestalten. Erfahrung mit Google BigQuery ist ein Plus. - Du verfügst über Erfahrung mit Machine Learning Framework wie XGBoost/LightGBM/CatBoost, Scikit-learn oder PyTorch/Tensorflow/Jax und hast idealerweise bereits mit Cloud Services wie GCP (oder auch Azure bzw. AWS) gearbeitet. - Du kennst moderne AI-Coding-Assistenten und Workflows bereits oder hast Lust, sie aktiv auszuprobieren und sinnvoll in Deine Arbeit zu integrieren. - Du bist mit agiler Arbeitsweise vertraut und kannst Dich schnell auf neue Anforderungen einstellen. - Du bist ehrgeizig die beste Lösung für Deine Fragestellungen zu finden und willst die Erwartung aller Kunden (intern und Endkunden) immer übertreffen. - Du hast verhandlungssichere Englischkenntnisse, Deutsch ist von Vorteil. Benefits - Freiwilliges Urlaubs- und Weihnachtsgeld sowie Zuschüsse zur Altersvorsorge. - 30 % Mitarbeitendenrabatt, auch für Deine:n Partner:in, inklusive Gastronomie und Friseur. - Ab dem 5. Jahr erhältst Du zusätzliche Urlaubstage. - Zuschuss zu Deiner Urban Sports Mitgliedschaft. - Von Trainings bis zu Führungsprogrammen. - Bis zu 30 Kalendertage im Jahr in ausgewählten europäischen Ländern. - Über Gehaltsumwandlung kannst Du Dir zusätzliche Urlaubstage sichern, ergänzt durch Sabbatical-Option. - Mit Company Bike Dein Wunschrad oder E-Bike unkompliziert leasen.

Title: Data Scientist Location: Sterling Heights United States Job Description: Job Title: Data Engineer Job Description This role focuses on designing, developing, and maintaining enterprise data infrastructure to support advanced analytics across engineering and business teams. The Data Engineer builds scalable data pipelines, ensures data integrity and accessibility, and enables reporting and dashboards that transform raw data into actionable insights. The position supports enterprise data architecture and governance, particularly across cloud platforms and business intelligence tools, while working closely with cross-functional stakeholders in a fast-paced engineering environment. Responsibilities - Design, build, and maintain scalable data pipelines and data storage solutions to support enterprise analytics and reporting. - Integrate data from multiple sources, including databases, APIs, and enterprise platforms, into centralized data warehouses or data lakes. - Transform raw data into structured, analytics-ready datasets that support dashboards, reports, and advanced analytics use cases. - Ensure data accuracy, consistency, and integrity through robust validation, quality control, and monitoring processes. - Optimize data pipelines and storage solutions for performance, scalability, and reliability in cloud and on-premises environments. - Support data architecture governance by identifying and maintaining authoritative sources of truth (ASOT) for key data domains. - Enable and manage data replication, transformation, and synchronization across enterprise platforms such as AWS and Power BI. - Collaborate with data scientists, analysts, engineers, and business stakeholders to gather requirements and translate them into technical data solutions. - Develop and maintain data models and documentation that support enterprise reporting and analytics. - Troubleshoot data issues, diagnose root causes, and implement corrective actions to maintain system performance and data quality. - Work cross-functionally with engineering and stakeholder teams to align data solutions with program and business objectives. - Apply strong analytical skills, including statistics, probability, and mathematical modeling, to support data-driven decision-making. - Participate in Agile/Scrum activities and contribute to continuous improvement of data engineering processes and practices. - Manage multiple priorities and projects in a fast-paced environment while maintaining high standards of quality and organization. Essential Skills - Proven experience in data engineering and data infrastructure design, including pipelines, data warehouses, and databases. - Strong proficiency in SQL and experience with Oracle databases. - Hands-on experience with AWS and Redshift for cloud-based data solutions. - Proficiency with Power BI for data transformation, reporting, and dashboard development. - Experience in data pipeline development using ETL/ELT tools and frameworks. - Practical experience with data validation, quality control, and data governance, including familiarity with authoritative sources of truth (ASOT). - Strong analytical skills, including knowledge of statistics, probability, and mathematical modeling. - Experience working cross-functionally with engineering teams and business stakeholders. - Strong written and verbal communication skills, with the ability to explain complex data concepts to non-technical audiences. - Ability to work both independently and collaboratively within team environments. - Demonstrated experience translating data into actionable insights that support decision-making. - Strong troubleshooting and problem-solving abilities in data and system environments. - Ability to manage multiple priorities effectively in a fast-paced setting. - High attention to detail and strong organizational skills. - Experience developing data requirements and data models to support analytics and reporting. Additional Skills & Qualifications - Experience with Midas, SSAS, Alation, and Dataiku for data management and analytics. - Familiarity with MATLAB/Simulink, Mathcad, and MagicDraw in engineering and modeling contexts. - Exposure to Agile/Scrum methodologies, including tools such as Jira or ServiceNow. - Knowledge of combat vehicle systems or defense-related programs. - Experience with structured problem-solving methodologies. - Understanding of Agile metrics and reporting practices. - Systems engineering knowledge or familiarity with engineering processes. Work Environment The position operates in a hybrid work model, with three days onsite and two days remote each week, providing a balance of in-person collaboration and focused remote work. The role follows a 9/80 schedule, offering every other Friday off while maintaining full-time hours. You will work in a fast-paced, cross-functional engineering environment alongside data scientists, analysts, and engineering teams, collaborating on complex, large-scale data systems that support advanced engineering programs. The environment offers exposure to modern data tools and cloud technologies such as AWS and Power BI and provides opportunities to contribute to enterprise-level data strategy and architecture. Job Type & Location This is a Contract to Hire position based out of Sterling Heights, MI. Pay and Benefits The pay range for this position is $53.00 - $54.00/hr. Eligibility requirements apply to some benefits and may depend on your job classification and length of employment. Benefits are subject to change and may be subject to specific elections, plan, or program terms. If eligible, the benefits available for this temporary role may include the following: - Medical, dental & vision - Critical Illness, Accident, and Hospital - 401(k) Retirement Plan - Pre-tax and Roth post-tax contributions available - Life Insurance (Voluntary Life & AD&D for the employee and dependents) - Short and long-term disability - Health Spending Account (HSA) - Transportation benefits - Employee Assistance Program - Time Off/Leave (PTO, Vacation or Sick Leave) Workplace Type This is a hybrid position in Sterling Heights,MI. About Actalent Actalent is a global leader in engineering and sciences services and talent solutions. We help visionary companies advance their engineering and science initiatives through access to specialized experts who drive scale, innovation and speed to market. With a network of almost 20,000 consultants and 5,000 clients across the U.S., Canada, Asia and Europe, Actalent serves many of the Fortune 500. We are proud to be an Engineering News-Record (ENR) Top 500 Design Firm for our engineering design services and a ClearlyRated Best of Staffing winner for both client and talent service.

Title: Staff Data Scientist, Finance & Business Ops Location: San Francisco, CA, US; Remote, US Job Description: About Pinterest: Millions of people around the world come to our platform to find creative ideas, dream about new possibilities and plan for memories that will last a lifetime. At Pinterest, we’re on a mission to bring everyone the inspiration to create a life they love, and that starts with the people behind the product. Discover a career where you ignite innovation for millions, transform passion into growth opportunities, celebrate each other’s unique experiences and embrace the flexibility to do your best work. Creating a career you love? It’s Possible. At Pinterest, AI isn't just a feature, it's a powerful partner that augments our creativity and amplifies our impact, and we’re looking for candidates who are excited to be a part of that. To get a complete picture of your experience and abilities, we’ll explore your foundational skills and how you collaborate with AI. Through our interview process, what matters most is that you can always explain your approach, showing us not just what you know, but how you think. You can read more about our AI interview philosophy and how we use AI in our recruiting process here. Pinterest is seeking an experienced Staff Data Scientist to join our Finance & Business Operations team. This is a hybrid data-science / applied-AI / product-engineering role inside Pinterest's CFO organization. It sits at the intersection of forecasting and finance analytics, internal tool-building, and AI adoption — and the person in it is expected to operate across all three. The core mandate is to make the CFO org's forecasting and planning work faster, be more rigorous, and more self-serve. In practice that has meant owning a forecasting product end to end (data pipeline through user-facing UI), partnering directly with Finance, BizOps, and Core/Monetization stakeholders to embed it in their workflows, and turning the company's emerging AI platform capabilities into tools that finance teams actually use day to day. What you'll do: - Own forecasting tooling end to end. Build and maintain the team's primary forecasting workbench — from the underlying data and forecast logic through the interactive web UI that planners use to create, adjust, and review forecasts. This spans baseline vs. adjusted forecast modeling, scenario/delta workflows, backtesting, and diagnostics (year-over-year and month-over-month seasonality, engagement rates, and similar). - Ship product, not just analysis. Design and build user-facing features: chart and visualization work, guided onboarding, history/audit views, region and time-grain filtering, performance optimization, and the kind of polish and bug-fixing that makes an internal tool feel like a real product. Instrument usage (collect and analyze raw logs) and let adoption data drive the roadmap. - Drive AI adoption across Finance & BizOps. Take platform-level AI capabilities and turn them into concrete, trusted tools for finance users. Bring structured business cases (not wishlists) to platform/IT partners, pilot new capabilities, and write the enablement material — walkthroughs, documentation, "where to get started" guidance — that gets non-technical teams productive. - Stay ahead of the AI capability curve. A significant part of this role is forward-looking: continuously read and interpret AI research (papers, model and tooling releases) and translate it into a grounded point of view on what will be possible in the next 6–12 months. Track the engineering roadmap closely, understand what platform capabilities are landing and when, and connect those dots to concrete opportunities for the CFO org — so the team builds for where AI is going, not just where it is today. - Set AI strategy and guide executives. Turn that capability foresight into strategy: shape the CFO org's AI roadmap, prioritize where to invest, and advise senior leaders and executives on what's real, what's hype, and what to bet on. Communicate complex AI and technical trade-offs in plain, decision-ready terms, and act as a trusted technical advisor in executive conversations. - Deliver recurring finance analytics. Support core CFO-org deliverables: budget-vs-actuals (BVAs), variance commentary, executive slide/deck preparation, and metric diagnostics (e.g., MAU and revenue diagnostics), including catching and resolving data-quality issues. - Partner broadly and communicate clearly. Work directly with Finance, BizOps, Monetization, and platform/IT stakeholders. Translate ambiguous business questions into tooling and analysis, post clear release notes and stakeholder updates, and run live walkthroughs and training sessions. - Set technical and analytical standards. raise the bar on rigor (validation, backtesting, sound metric definitions), make pragmatic build-vs-buy and scope calls, and create artifacts and documentation durable enough to outlive any single contributor. - What we’re looking for: - Data science & forecasting - Strong applied background in time-series forecasting and quantitative analysis: baseline construction, scenario/adjustment modeling, backtesting and forecast-accuracy evaluation, and seasonality analysis (y/y, m/m). - Fluency in turning messy business questions into well-defined metrics and diagnostics; rigorous about metric definitions, data quality, and validation. - Advanced SQL and proficiency in a primary analysis language (Python strongly preferred); comfort working directly with data warehouses and large datasets. - Engineering & tool-building - Demonstrated ability to build and ship internal web tools, not just notebooks or one-off analyses — meaningful front-end / full-stack capability (e.g., JavaScript/TypeScript, modern UI frameworks, interactive data visualization). - Practical product-engineering instincts: UX/usability sense, performance debugging and optimization, handling state/data edge cases, and disciplined release hygiene (testing, build/lint, changelogs). - Experience building dashboards and self-serve analytics (e.g., Superset, Tableau, Looker, or equivalent). - Applied AI - Hands-on experience applying modern AI/LLM tooling to real workflows — prototyping with AI assistants, agentic/MCP-style tooling, or internal AI platforms — and a track record of moving from experiment to adopted tool. - Ability to build the business case for AI investment and to drive adoption with non-technical users (enablement, documentation, training). - AI foresight & strategy (critical) - Demonstrated habit of staying current with AI research and the broader landscape: able to read papers and model/tooling release notes and form a credible, independent view of what will be feasible 6–12 months out. - Able to interpret an engineering roadmap and reconcile it with where the technology is heading — translating both into a concrete capability plan for the business. - Strong product/business strategy instincts: prioritizing AI investments, sequencing bets, and distinguishing durable capability from hype. - Executive influence - Proven ability to advise and guide senior leaders and executives on technical and AI strategy, and to make complex trade-offs legible to a non-technical executive audience. - Comfortable being the trusted technical voice in the room — framing decisions, managing expectations, and earning credibility with both finance leadership and engineering/platform partners. - Scope, ownership & communication - Staff-level autonomy: can independently scope, prioritize, and deliver multi-month efforts with minimal direction, and make sound trade-off calls. - Excellent written and verbal communication; can write for executives and for end users, and can run live training and walkthroughs. - Strong cross-functional collaboration across finance, operations, and technical/platform partners. - Experience - Minimum of 8 years of relevant experience in data science, analytics engineering, or applied ML. - Bachelor's degree in a quantitative field (e.g., statistics, computer science, economics, engineering, math) or equivalent practical experience; advanced degree is a plus. - Relocation Statement: - This position is not eligible for relocation assistance. Visit our PinFlex page to learn more about our working model. In-Office Requirement Statement: - We recognize that the ideal environment for work is situational and may differ across departments. What this looks like day-to-day can vary based on the needs of each organization or role. - This role will need to be in the office for in-person collaboration1-2 times every 6-months and therefore can be situated anywhere in the country. - #LI-NM4 #LI-REMOTE At Pinterest we believe the workplace should be equitable, inclusive, and inspiring for every employee. In an effort to provide greater transparency, we are sharing the base salary range for this position. The position is also eligible for equity. Final salary is based on a number of factors including location, travel, relevant prior experience, or particular skills and expertise. Information regarding the culture at Pinterest and benefits available for this position can be found here. US based applicants only $164,695—$339,078 USD Our Commitment to Inclusion: Pinterest is an equal opportunity employer and makes employment decisions on the basis of merit. We want to have the best qualified people in every job. All qualified applicants will receive consideration for employment without regard to race, color, ancestry, national origin, religion or religious creed, sex (including pregnancy, childbirth, or related medical conditions), sexual orientation, gender, gender identity, gender expression, age, marital status, status as a protected veteran, physical or mental disability, medical condition, genetic information or characteristics (or those of a family member) or any other consideration made unlawful by applicable federal, state or local laws. We also consider qualified applicants regardless of criminal histories, consistent with legal requirements.