AI & Machine Learning Engineer

LatentView Analytics is a leading global analytics and decision sciences provider, delivering solutions that help companies drive digital transformation and use data to gain a competitive advantage. With analytics solutions that provide a 360-degree view of the digital consumer, fuel machine learning capabilities, and support artificial intelligence initiatives., LatentView Analytics enables leading global brands to predict new revenue streams, anticipate product trends and popularity, improve customer retention rates, optimize investment decisions, and turn unstructured data into valuable business assets.

We are seeking a highly skilled AI & Machine Learning Engineer to build, optimize, deploy, and operate production-grade machine learning models that power B2B Go-to-Market (GTM) prioritization, lead scoring, engagement prediction, and revenue intelligence initiatives.

In this role, you will work across the entire machine learning lifecycle—from large-scale feature engineering and model development to deployment, monitoring, and continuous optimization. You will collaborate closely with data engineers, analysts, and business stakeholders to transform complex data into actionable insights that drive sales and marketing effectiveness.

This is a hands-on position within a high-impact team where you'll own model performance end-to-end and contribute to the evolution of our AI-driven decision-making platform.

Key Responsibilities

• Design, develop, and maintain production AI/ML models for lead scoring, account prioritization, propensity modeling, and engagement prediction.
• Build scalable feature engineering pipelines on multi-billion-row datasets using Python, SQL, Spark, and cloud data platforms.
• Train, tune, evaluate, and optimize supervised learning models with a focus on classification and ranking problems.
• Implement advanced machine learning techniques using XGBoost, LightGBM, Random Forest, and related algorithms.
• Apply model explainability techniques such as SHAP values, feature importance analysis, and partial dependence plots.
• Develop automated model retraining, monitoring, and performance tracking workflows.
• Optimize large-scale data pipelines for performance, reliability, and cost efficiency.
• Investigate data quality issues and assess their impact on AI/ML model outcomes.
• Collaborate with business stakeholders to translate GTM objectives into machine learning solutions.
• Contribute to shared ML infrastructure, best practices, and MLOps initiatives.
• Communicate technical findings, model insights, and business impact to both technical and non-technical audiences.

Required Qualifications

Machine Learning & AI

• Strong foundation in Machine Learning and predictive analytics.
• Hands-on experience with supervised learning algorithms, especially:
• XGBoost
• LightGBM
• Random Forest
• Experience building classification models on imbalanced datasets.
• Knowledge of model evaluation metrics, validation strategies, and performance optimization.
• Experience with hyperparameter tuning using Optuna, Hyperopt, Grid Search, or Random Search.
• Familiarity with explainable AI (XAI) techniques, including SHAP and feature importance methods.
• Understanding of model deployment, versioning, monitoring, and lifecycle management.

Programming

• Strong Python skills including:
• pandas
• NumPy
• scikit-learn
• Experience with modern software development and packaging practices.
• Advanced SQL skills:
• Complex joins
• Window functions
• CTEs
• Query optimization
• Ability to understand, maintain, and improve legacy codebases.

Data Engineering & Platforms

• Experience with Databricks or equivalent cloud data platforms.
• Working knowledge of Apache Spark (PySpark and/or Spark SQL).
• Familiarity with Delta Lake, Parquet, or similar large-scale storage formats.
• Experience with notebook-driven analytics and development workflows.

Engineering Practices

• Proficiency with Git and collaborative software development.
• Experience with testing, code reviews, and CI/CD practices.
• Strong documentation and knowledge-sharing skills.
• Ability to write maintainable, scalable, and production-ready code.

Preferred Qualifications

• Experience working with B2B sales, marketing, or CRM datasets.
• Familiarity with Salesforce, Marketo, Adobe Experience Platform, or similar enterprise platforms.
• Experience developing:
• Lead scoring models
• Propensity models
• Customer engagement scoring systems
• Revenue intelligence solutions
• Exposure to MLOps platforms such as MLflow, Model Registry, and automated retraining pipelines.
• Familiarity with Unity Catalog or other governed data environments.
• Experience with experimentation frameworks and A/B testing methodologies.
• Knowledge of modern AI workflows, feature stores, and production ML systems.

At LatentView Analytics, we value a diverse, inclusive workforce and provide equal employment opportunities for all applicants and employees. All qualified applicants for employment will be considered without regard to an individual's race, colour, sex, gender identity, gender expression, religion, age, national origin or ancestry, citizenship, physical or mental disability, medical condition, family care status, marital status, domestic partner status, sexual orientation, genetic information, military or veteran status, or any other basis protected by federal, state or local laws.