About impact.com
impact.com is the world’s leading commerce partnership marketing platform, transforming the way businesses grow by enabling them to discover, manage, and scale partnerships across the entire customer journey. From affiliates and influencers to content publishers, brand ambassadors, and customer advocates, impact.com empowers brands to drive trusted, performance-based growth through authentic relationships. Its award-winning products - Performance (affiliate), Creator (influencer), and Advocate (customer referral) - unify every type of partner into one integrated platform. As consumers increasingly rely on recommendations from people and communities they trust, impact.com helps brands show up where it matters most. Today, over 5,000 global brands - including Walmart, Uber, Shopify, Lenovo, L’Oréal, and Fanatics - rely on impact.com to power more than 350,000 partnerships that deliver measurable business results.

Your Role at impact.com:

We're seeking a Senior Data Scientist to serve as an embedded Data Scientist within our Programmatic Experience Group. You'll own the design and deployment of machine learning models that optimize yield, pricing, and inventory allocation at scale — sitting at the intersection of data science, platform engineering, and marketplace economics.

This is a high-craft, high-ownership individual contributor role. You'll work end-to-end: architecting the data pipelines that feed your models, engineering the features that drive performance, and deploying real-time inference systems that make decisions at speed. Your work directly determines how effectively Impact's programmatic marketplace balances advertiser performance with publisher monetization — making this one of the highest-leverage technical roles in the business.

You'll collaborate closely with Product, Data Science, and Programmatic Delivery Engine Engineering, but you operate with significant autonomy. You're expected to bring both the modeling rigor of a data scientist and the production instincts of an ML engineer — and to be genuinely excited about both.

What You'll Do:

Yield Optimization & Pricing Models

• Design and deploy ML models that optimize auction pricing, bid shading, floor price setting, and yield across Impact's programmatic inventory.
• Build and iterate on real-time pricing algorithms that balance short-term revenue efficiency with long-term publisher and advertiser health.
• Develop and maintain feedback loops that allow pricing models to adapt to shifting market conditions, inventory mix, and demand patterns.
• Quantify the revenue impact of pricing model improvements; communicate tradeoffs between yield maximization, fill rate, and partner ROI to stakeholders.

Inventory Allocation & Supply Optimization

• Own ML-driven inventory allocation logic: routing, pacing, and matching supply to demand across partner segments, deal types, and campaign objectives.
• Build models that forecast inventory availability, demand curves, and clearing prices to support proactive allocation decisions.
• Identify and address inefficiencies in inventory utilization — including unsold inventory, suboptimal deal matching, and allocation imbalances across the publisher base.

Data Architecture & Feature Engineering

• Design and own the data infrastructure that feeds programmatic models: event pipelines, feature stores, training datasets, and real-time feature serving.
• Engineer high-signal features from auction logs, bid stream data, user signals, contextual attributes, and historical performance — at the scale of programmatic data volumes.
• Build robust data pipelines with production-grade standards: reliability, observability, versioning, and efficient reprocessing.

Real-Time Inference & Production ML

• Deploy models to production real-time inference environments; own latency, reliability, and throughput requirements for auction-time decision-making.
• Build monitoring systems that track model performance, data drift, and system health in production; define alerting thresholds and retraining triggers.
• Partner with MLOps and Platform Engineering to ensure scalable, low-latency serving infrastructure meets SLOs under high-volume auction traffic.
• Own the full model lifecycle: training, evaluation, deployment, A/B testing, and iteration.

Experimentation & Performance Measurement

• Design and execute rigorous A/B and holdout experiments to measure the causal impact of model changes on yield, fill rate, advertiser performance, and publisher revenue.
• Build evaluation frameworks that go beyond offline metrics — validating model behavior in live auction environments where feedback signals are delayed or noisy.
• Translate experimental results into clear business narratives; present findings and recommendations to Product and business stakeholders.

Self-Learning Systems & Feedback Loops

• Research and implement adaptive, self-learning components within the programmatic stack — including contextual bandits, reinforcement learning signals, and online learning approaches where appropriate.
• Design feedback mechanisms that close the loop between auction outcomes, model updates, and system behavior; reduce reliance on manual tuning and rule-based overrides.
• Stay current with advances in programmatic ML, auction theory, and online optimization; evaluate applicability to Impact's specific marketplace dynamics.

Cross-Functional Collaboration

• Serve as the primary ML technical partner for the Rubicon product and engineering teams; translate business requirements into modeling approaches and communicate technical tradeoffs clearly.
• Collaborate with Data Science peers on shared infrastructure, modeling standards, and cross-domain feature reuse.
• Document models, architectures, and experimental findings to a standard that enables review, replication, and knowledge transfer across teams.

What You Bring:

Required

• Experience: 5+ years in data science, ML engineering, or quantitative research, with at least 2+ years building and deploying ML models in programmatic advertising, ad tech, marketplace optimization, or a closely related domain (e.g., real-time bidding, dynamic pricing, auction systems).
• Programmatic & marketplace depth: Demonstrated understanding of programmatic auction mechanics (RTB, header bidding, floor pricing, deal types, bid shading) and how ML can be applied to optimize outcomes across the supply-demand stack.
• Production ML engineering: Proven ability to take models from prototype to production independently — including real-time inference, monitoring, retraining pipelines, and SLO ownership.
• Data architecture: Experience designing and building data pipelines, feature stores, and training infrastructure for high-volume, low-latency ML systems.
• Technical skills:
• Strong Python and SQL; proficiency with ML libraries (scikit-learn, XGBoost, LightGBM, PyTorch/TensorFlow) and large-scale data tools (Spark, Kafka, or equivalent streaming/batch frameworks).
• Experience with real-time feature serving and low-latency model deployment (REST APIs, gRPC, or streaming inference).
• Familiarity with production ML workflows: model versioning, drift monitoring, A/B testing, evaluation, and retraining.
• Experience processing and modeling at programmatic data scale: high-cardinality auction logs, bid stream data, impression and click events.
• Experimentation rigor: Strong grasp of causal inference and experiment design in online, delayed-feedback environments (auction holdouts, switchback tests, variance reduction techniques).
• Communication: Ability to explain complex modeling decisions and tradeoffs to Product and business stakeholders; comfortable presenting in cross-functional forums.
• Education: Bachelor's in a quantitative field (CS, Statistics, Math, Engineering, Economics, or similar); Master's/PhD preferred.

Preferred / Nice to Have

• Direct experience with SSP, DSP, or exchange-side yield optimization — particularly floor price optimization, bid landscape modeling, or deal matching algorithms.
• Familiarity with auction theory (first-price vs. second-price dynamics, optimal reserve pricing, revenue equivalence) and its practical implications for programmatic ML.
• Experience with contextual bandits, multi-armed bandits, or reinforcement learning applied to real-time decisioning problems.
• Knowledge of online learning and adaptive algorithms in production environments with non-stationary data distributions.
• Familiarity with privacy-preserving ML techniques relevant to programmatic (differential privacy, federated learning, cookieless attribution modeling).
• Experience with GCP tools (BigQuery, Vertex AI, Dataflow, Pub/Sub) and/or Databricks/Spark for large-scale event processing and model training.
• Exposure to supply forecasting, inventory management, or capacity planning in programmatic or marketplace contexts.
• Familiarity with Impact's affiliate and partnership ecosystem, or prior experience at the intersection of performance marketing and programmatic delivery.

What Sets You Apart

• Marketplace intuition. You understand programmatic auctions not just as an engineer but as an economist — you think about incentive structures, equilibrium dynamics, and how model decisions ripple through the supply-demand stack.
• Full-stack ML ownership. You're as comfortable designing a feature store schema as you are tuning a gradient boosting model or debugging a latency spike in production. You own the whole chain.
• Feedback loop thinking. You don't just deploy models — you design the systems that make them smarter over time. You think about how today's decisions become tomorrow's training signal.
• Rigor under real-world constraints. You know how to run clean experiments in environments where feedback is delayed, data is noisy, and business pressures create tradeoffs. You don't let imperfect conditions become an excuse for imprecise thinking.
• Pragmatic delivery. You ship. You balance the perfect with the production-ready, iterate fast, and know when an MVP outperforms a six-month research project.
• Collaborative depth. You build genuine technical trust with engineering and product partners — not just by having good ideas, but by following through, communicating clearly, and making the integration easy.

Salary Range: $165,000 - $185,000 per year, plus an additional 5% variable annual bonus contingent on Company performance and eligible to receive a Restricted Stock Unit (RSU) grant.

*This is the pay range the Company believes is equitable for this position at the time of this posting. Consistent with applicable law, compensation will be determined based on the skills, qualifications, and experience of the applicant along with the requirements of the position, and the Company reserves the right to modify this pay range at any time.

Benefits and Perks: 

At impact.com, we believe that when you’re happy and fulfilled, you do your best work. That’s why we’ve built a benefits package that supports your well-being, growth, and work-life balance.

• Strong extended health benefits: Health & Prescription coverage, vision and dental care, virtual health care, out-of-country medical coverage
• Insurance coverage (life, short-term disability, long-term disability, and more)
• Health Care Spending Account
• Two different Employee Assistance Programs
• Flexible Working: Our Responsible PTO policy means you can take the time off you need to rest and recharge. We're committed to a positive work-life balance and provide a flexible environment that allows you to be happy and fulfilled in both your career and your personal life.
• Health and Wellness: Your well-being is a priority. Our mental health and wellness benefit includes up to 12 fully covered therapy/coaching sessions per year, with additional dependent coverage. We also offer a monthly gym reimbursement policy to support your physical health.
• A Stake in Our Growth: We offer Restricted Stock Units (RSUs) as part of our total compensation, giving you a stake in the company's growth with a 3-year vesting schedule, pending Board approval.
• Investing in Your Growth: We’re committed to your continuous learning. Take advantage of our free Coursera subscription and our PXA courses.
• Parental Support: We offer a generous parental leave policy, 26 weeks of fully paid leave for the primary caregiver and 13 weeks fully paid leave for the secondary caregiver.
• Technology Financial Support: We provide a technology stipend to help you set up your home office and a monthly allowance to cover your internet expenses. 

Note: Benefits may vary by location and are subject to local regulations.

impact.com is proud to be an equal-opportunity workplace. All employees and applicants for employment shall be given fair treatment and equal employment opportunity regardless of their race, ethnicity or ancestry, color or caste, religion or belief, age, sex (including gender identity, gender reassignment, sexual orientation, pregnancy/maternity), national origin, weight, neurodivergence, disability, marital and civil partnership status, caregiving status, veteran status, genetic information, political affiliation, or other prohibited non-merit factors.

#LI_Vancouver_Canada

MaintainX est la première plateforme mobile au monde dédiée à la gestion des actifs et des tâches dans les environnements industriels et de première ligne. Nous proposons une solution moderne, basée sur l'IoT et le cloud, qui facilite la maintenance, la sécurité et l'exploitation des équipements et installations physiques. Nous aidons plus de 12 000 organisations, dont Duracell, Univar Solutions, Titan America, McDonald's, Brenntag, Cintas, Xylem et Shell, à atteindre l'excellence opérationnelle et la fiabilité à grande échelle. À la suite de notre série D de 150 millions de dollars menée par Bain Capital Ventures, Bessemer Ventures, August Capital, Amity Ventures et Ridge Ventures, MaintainX a levé un total de 254 millions de dollars, valorisant la société à 2,5 milliards de dollars. Alors que nous entrons dans notre prochaine phase de croissance, nous investissons massivement dans l'IA/ML, les LLM et l'IoT industriel afin de transformer le mode de fonctionnement des équipes de première ligne, en prédisant les pannes avant qu'elles ne se produisent, en automatisant les flux de travail et en intégrant l'intelligence dans chaque actif et chaque procédure.

Ce que vous ferez:

• Concevoir, développer et optimiser des modèles d’apprentissage automatique (machine learning) pour la détection et la classification de défauts de bout en bout — des choix liés aux données et à l’entraînement jusqu’aux stratégies d’évaluation et aux contraintes de mise en production.
• Réaliser des analyses exploratoires de données (EDA) sur des données de vibration, des données OT (technologies opérationnelles) et des séries temporelles afin d’en tirer des insights et d’identifier des motifs révélateurs de défaillances ou d’anomalies.
• Mener des expérimentations et évaluer différents algorithmes en modélisation de séries temporelles, en traitement du signal et en méthodes statistiques afin d’optimiser la performance des modèles.
• Collaborer avec les chefs de produit (PM) pour la découverte de fonctionnalités et la priorisation de la feuille de route, notamment en validant des hypothèses produit, en définissant des indicateurs de succès et en quantifiant l’impact pour les utilisateurs finaux.
• Travailler en étroite collaboration avec des experts du domaine afin de valider les résultats et d’assurer leur pertinence dans des contextes réels.
• Participer activement à la communauté interne de pairs pour remettre en question le statu quo, améliorer les méthodes de travail et influencer les décisions d’architecture, tout en contribuant à une culture d’excellence en science appliquée.
• Participer aux rotations de garde (on-call).

À propos de vous:

• Maîtrise ou doctorat en informatique, science des données, génie mécanique, génie électrique ou dans un domaine connexe, avec une spécialisation en surveillance d’état (condition monitoring) ou en apprentissage automatique.
• Plus de 5 ans d’expérience démontrée en programmation avec des outils standards de ML tels que Python, PyTorch, TensorFlow, etc.
• Solides connaissances fondamentales en apprentissage automatique, en science des données et en statistiques.
• Bonne maîtrise des techniques de modélisation de séries temporelles et de l’ingénierie de caractéristiques (feature engineering).
• Capacité à livrer du code prêt pour la production, bien testé, maintenable et validé par des expérimentations rigoureuses.

Atouts:

• Expérience pratique dans le développement de modèles appliqués aux données OT et de vibration, à la surveillance d’état et à la détection ou classification de défauts.
• Connaissance des techniques de traitement du signal (p. ex. transformée de Fourier, analyse en ondelettes) et de leur application aux données OT et de vibration.

Quels sont les avantages pour vous?:

• Un salaire compétitif et des opportunités d'équité significatives.
• Couverture des soins de santé, des soins dentaires et de la vue.
• Programme d'inscription 401(k) / RRSP.
• Prenez les congés dont vous avez besoin.
• Une culture du travail :
• Vous travaillerez avec des personnes intelligentes, humbles et optimistes à travers le monde.
• Un environnement méritocratique où les idées et les résultats sont publiquement célébrés.

Qui sommes-nous:

MaintainX is the world’s leading mobile-first Asset and Work Intelligence platform for industrial and frontline environments. We’re a modern, IoT-enabled, cloud-based solution that powers maintenance, safety, and operations on physical equipment and facilities.

We help 12,000+ organizations—including Duracell, Univar Solutions, Titan America, McDonald’s, Brenntag, Cintas, Xylem, and Shell—achieve operational excellence and reliability at scale.

Following our $150 million Series D led by Bain Capital Ventures, Bessemer Ventures, August Capital, Amity Ventures, and Ridge Ventures, MaintainX has raised a total of $254 million, valuing the company at $2.5 billion.

As we enter our next phase of growth, we’re investing deeply in AI/ML, LLMs, and Industrial IoT to transform how frontline teams operate—predicting failures before they happen, automating workflows, and embedding intelligence into every asset and procedure.

What you’ll do:

• Design, develop and optimize machine learning models for fault detection and classification end-to-end e.g. data and training modeling choices to evaluation strategies and production constraints.
• Perform EDA on vibration, OT and time-series data to uncover insights and identify patterns indicative of faults or anomalies.
• Conduct experiments and evaluation of various algorithms on time-series modeling, signal processing, and statistical methods, to optimize model performance.
• Partner with PMs in product feature discovery and roadmap prioritization through validating product hypotheses, designing success metrics and quantifying end user impact
• Collaborate with domain experts to validate findings and ensure alignment with real-world applications.
• Engage with your community of peers to challenge the status quo, improve our shared ways of working, and influence overall architecture decisions, continuing to foster our culture of Applied Science excellence
• On-call duties

About you:

• Master’s or Ph.D. in Computer Science, Data Science, Mechanical Engineering, Electrical Engineering, or a related field with a focus on condition monitoring or machine learning applications.
• 5+ years of proven programming skills using standard ML tools such as Python, PyTorch, Tensorflow etc.
• Strong foundational knowledge in machine learning, data science, and statistics
• Familiarity with time-series modeling techniques and feature engineering.
• Ability to deliver production-grade code that is well-tested, maintainable, and evaluated through rigorous experimentation.

Bonus skills: 

• Hands-on experience developing models for OT and vibration analysis, condition monitoring, and fault detection or classification.
• Familiarity with signal processing techniques (e.g., Fourier transforms, wavelet analysis) and their application to OT and vibration data.

What’s in it for you:

• Competitive salary and meaningful equity opportunities.
• Healthcare, dental, and vision coverage.
• 401(k) / RRSP enrollment program.
• Take what you need PTO.
• A high impact Culture:
• You’ll work with Smart, Humble Optimists across the globe.
• Meritocratic environment where ideas and outcomes are publicly celebrated.

About us:

We exist to make the lives of frontline and maintenance teams easier by building software that meets their real-world needs. Our product transforms how 80% of the global workforce—those who don’t sit behind a desk—manage their operations, assets, and teams.

MaintainX is committed to creating a diverse environment. All qualified applicants will receive consideration for employment without regard to race, color, religion, gender, gender identity or expression, sexual orientation, national origin, genetics, disability, age, or veteran status.

The Team:

Our Business Applications and Analytics team plays a pivotal role in driving business success through data-driven decision-making and operational excellence. This dynamic team manages enterprise-wide applications, including ERP, CRM, and BI systems. By harnessing the power of data and advanced analytical tools, we provide valuable insights that inform strategic initiatives, optimize processes, and enhance overall performance.  

Position Overview:  

Diligent is ‘All-In’ on AI and is seeking a dynamic and strategic AI leader to join our team as an AI Solutions Architect. As part of the Business Applications and Analytics team, this crucial role is designed to accelerate our Internal AI capabilities across multiple business functions, including Marketing, GTM/Sales, Customer Success & Support, Finance, HR, Legal, and Product and Engineering teams. 

The primary goal of this position is to identify, prototype, deploy, and scale AI technologies and solutions that drive greater operational efficiency and effectiveness. The ideal candidate will be a technically adept individual with business acumen, able to bridge the gap between robust technical implementations and business needs, with the ability to partner with various business leaders to spot and act on opportunities for AI-driven transformation and innovation. 

Key Responsibilities:

• Partner with business leaders to understand challenges, identify AI opportunities, and implement effective AI/GenAI solutions.
• Lead the ideation, prototyping, deployment, and scaling of AI/GenAI technologies and agentic solutions tailored to business needs.
• Develop and communicate strategic frameworks and roadmaps for AI initiatives aligned with business goals.
• Champion best practices in AI/ML and GenAI development—including responsible AI, privacy, security, and ethical guidelines.
• Collaborate with cross-functional teams to integrate AI tools within core business processes, ensuring seamless, high-value adoption.
• Stay abreast of AI best practices, tools, and methodologies and introduce them to the organization.
• Drive AI and Data literacy across the company through training, workshops, and continuous education initiatives.
• Ensure compliance with data protection and privacy regulations in all AI implementations.
• Monitor and evaluate the performance and effectiveness of AI solutions and continually refine them.
• Collaborate with stakeholders to define and achieve key performance indicators (KPIs) and return on investment (ROI) for AI projects.
• Advocate and implement AI ethical guidelines and best practices.

Required Qualifications:

• Solid Foundations: Bachelor’s degree in Computer Science, AI/ML, Data Science, or a related technical field.
• Practical Tinkerer: 5+ years in a technical role (software, data, DevOps, etc.), with hands on experience building end-to-end prototypes using GenAI tools (GPTs, Hugging Face, open-source LLMs, LangChain, Scikit-learn, etc.).
• Curious Explorer: Demonstrates a hands-on approach to learning in prompt-engineering, fine-tuning, RAG pipelines, or API Integrations, with a commitment to rapid failure, iteration, and sharing findings.
• Data Wrangler: Possesses the ability to source, clean, and transform raw data, including spreadsheets, logs, APIs, or CRM exports, into structured inputs for AI experiments and prototypes.
• Code Crafter: Capable of writing scripts or small applications to connect models, cloud services, or data sources. Prior experience with TensorFlow, PyTorch, Azure, AWS, or GCP is beneficial, but willingness to learn is equally important.
• Business Translator: Experienced as a technical sales engineer or solutions consultant, capable of turning AI experiments into compelling stories and metrics that resonate with non-technical stakeholders.
• Responsible AI Mindset: Demonstrated experience in addressing privacy, security, bias, and ethics in professional work, with a strong familiarity with industry-specific data privacy, AI ethics, and compliance requirements.
• Team Player: Essential qualities include clear communication and a growth mindset, as you will collaborate with marketers, sales professionals, engineers and leaders.

Preferred Qualifications:

• Demonstrates strong knowledge of AI use cases, including NLP, computer vision, data analytics, predictive modelling, and translating technical capabilities into business value, along with proficiency in large language models (LLMs), prompt engineering, RAG architectures, MLOps, LLMOps, and GenAI application deployment.
• Advanced programming and data engineering skills, with Python preferred, and experience in cloud-based AI solutions (AWS, GCP, Azure).
• Ability to architect and deploy enterprise-scale AI solutions, collaborating across sales, product, and implementation teams to ensure adoption and measurable ROI.
• Strategic thinker with strong analytical and problem-solving skills, capable of driving measurable business outcomes through AI/GenAI.