Senior Lead AI/ML Engineer - R01555564

Primary Skills

• univariate and multivariate time series forecasting models
• Machine Learning

Job requirements

• Key Responsibilities
• Partner with product and business teams to define problems and translate them into data-driven solutions.
• Conduct exploratory data analysis (EDA) and extract actionable insights from structured and unstructured datasets.
• Develop, validate, and iterate on predictive models using techniques in supervised, unsupervised, and/or time series learning.
• Communicate modeling outcomes through clear visualizations and presentations to both technical and non-technical stakeholders.
• Build and maintain robust pipelines for model training, evaluation, and inference.
• Deploy machine learning models into production with attention to scalability, performance, and observability.
• Monitor model drift and performance over time and develop retraining and versioning strategies.
• Collaborate with software and data engineering teams to integrate ML solutions into end-user applications and internal systems.


• Qualifications Required:
• Extensive hands-on experience in univariate and multivariate time series forecasting models, ideally experience with models such as LGBM, Prophet, or similar.
• Deployment experience, including taking forecasting models into production.
• Proper vetting prior to scheduling interviews with our team to ensure alignment with these expectations.
• Master’s plus degree in Computer Science, Statistics, Applied Mathematics, or a related field.
• 5+ years of experience in data science and machine learning, with a proven track record of delivering models to production.
• Proficiency in Python and ML libraries such as scikit-learn, XGBoost, LightGBM, PyTorch, or TensorFlow.
• Strong understanding of statistical modeling, machine learning algorithms, and experiment design.
• Solid experience with SQL and data manipulation tools (e.g., Pandas, Spark, or Dask).
• Experience deploying models using APIs (Flask, FastAPI), Docker, and orchestration tools (e.g., Airflow, Kubeflow, MLflow).
• Hands-on experience with cloud platforms (AWS, GCP, or Azure) and model serving tools.
• Excellent problem-solving and communication skills; able to explain complex concepts clearly and effectively.


Preferred:
• Experience with time series forecasting, causal inference, recommendation systems, or NLP.
• Familiarity with data versioning and reproducibility tools (e.g., DVC, Weights & Biases).
• Exposure to feature stores, streaming data (e.g., Kafka), or real-time ML systems.
• Background in MLOps and experience building generalizable ML frameworks or platforms.
• Here is some additional context that we have put together regarding what we are looking for: Core


• Technical Skills ML Engineer Preferred: Ideally, the candidate should be an ML Engineer, though seasoned Data Scientists with relevant experience are suitable.
• Python & SQL: Strong coding and data manipulation skills.
• Time-Series Forecasting: Experience with LGBM (LightGBM) and Darts library. MLOps Expertise Preferred: Hands-on experience with Astronomer, Airflow, and DAG creation.
• Capable of building wrappers and scalable pipelines.
• This skill is highly valuable, but not a deal breaker. Cloud Platforms: Proficient in AWS, with exposure to GCP preferred.
• Debugging & Troubleshooting: Skilled in investigating and resolving issues in Python experiments and executions.
• GitHub Proficiency: Comfortable working in repositories with many contributors, managing branches, pull requests, and code reviews. Collaboration & Work Style
• Self-Starter: Able to work independently and proactively contribute ideas.
• Team-Oriented: Willing to support Roman and Calvin while offering directional guidance on model enhancements.
• Fast Learner: Quick to adapt to new tools, workflows, and business contexts to rapidly onboard into the project.
• Domain Expertise Sales Forecasting: Proven experience in building and refining forecasting models. Understanding of business KPIs and translating insights into action.