The Role:

The Senior Applied Scientist (AI/ML Customer Science) plays a critical role in designing, developing, and deploying machine learning, causal, and optimization systems that power Marketing and Sales decision-making. This role focuses on transforming complex customer behavior and business growth problems into measurable objectives and deployable ML systems across acquisition, conversion, retention, and revenue optimization.

You will work at the intersection of machine learning, experimentation, causal inference, and business strategy, building models and platforms that drive impact across areas such as:

• Sales prioritization & expected value modeling

• Marketing targeting & uplift modeling

• Multi-touch attribution & incrementality measurement

• Lifecycle modeling (conversion, retention, churn, LTV)

• Outreach optimization & personalization systems

You:

The preferred candidate will have a robust background in machine learning, optimization, forecasting, and causal inference, particularly within Marketing applications or closely related fields. You will be involved in every stage of the ML development pipeline - from data acquisition and ingestion to analysis, prototyping experimentation, and deployment. You should be able to thrive and succeed in an entrepreneurial setting, working collaboratively in a fast-paced environment with multiple stakeholders.


Responsibilities:

• Research, design, and develop machine learning, statistical, and causal models for Marketing and Sales use cases including:

  • Propensity & expected value modeling

  • Uplift and treatment effect estimation

  • Attribution and marketing measurement

  • Forecasting, optimization, and prioritization systems

• Translate real-world business growth problems into well-defined objectives, loss functions, and evaluation frameworks.

• Design feature representations capturing customer behavior, engagement, value, and lifecycle dynamics.

• Design and analyze experiments (A/B, A/A, geo tests, incrementality tests, diff-in-diff, causal impact).

• Build and maintain robust offline and online evaluation frameworks aligned to business outcomes (ROI, CAC, conversion, retention, GMV/LTV).

• Partner with Marketing and Sales to define success metrics, guardrails, and decision thresholds.

• Own the end-to-end ML lifecycle: data acquisition, feature engineering, training pipelines, evaluation, deployment, and monitoring.

• Develop scalable, maintainable code and ML services that integrate into business workflows (CRM, marketing platforms, internal tools).

• Partner with Data Engineering and Platform teams to productionize models and experimentation frameworks.

• Work closely with Marketing, Sales, Product, and Engineering leaders to shape strategy and execution.

• Clearly communicate insights, model behavior, limitations, and business implications to technical and non-technical stakeholders.

• Influence roadmap prioritization by connecting ML investments to revenue and growth impact.

• Provide technical leadership on system design, modeling choices, and evaluation standards.

• Mentor junior scientists and engineers; help establish Customer Science best practices.

• Contribute to the company's technical direction, reusable ML platforms, and long-term data science vision.

Minimum Requirements:

• 5+ years of industry experience in applied Machine Learning

• Masters Degree or PhD in CS / ML, statistics, operation research or related field

• 3+ years experience in building, deploying, and managing machine learning and deep learning models in production environments at scale

• Deep understanding of ML best practices (A/B testing, training/serving pipelines, feature engineering etc), algorithms/techniques (gradient boosting, deep neural networks, transformers, optimization, regularization), and experiment design

• Experience in Computer Vision. Additional experience in Causal Inference is highly desirable.

• Extensive experience in scientific libraries in Python (numpy, pandas, polars) and Machine Learning tools and frameworks (PyTorch, Tensorflow, Keras, Scikit-Learn)

• Strong data engineering skills and experience working with large scale datasets

• Experience with big data tools (Apache Beam, Apache Kafka, Spark)

• Experience with cloud technologies AWS, GCP or Azure

• Fluency in Python, SQL

Preferred Requirements:

• PhD preferred (CS, ML, AI, Stats, OR or related field)

• Background in applying ML techniques to solve real-world business problems in the retail sector.

• Familiarity with MLOps tools and pipelines.

• Impact-focused and passionate about delivering high-quality models.

• Demonstrated leadership experience, with the ability to lead and inspire a team.