Role overview
**Senior Lead AI/ML Engineer**
**Primary Skills**
* Univariate and multivariate time series forecasting models
* Sales Forecasting
* 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.
What we're looking for
* 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.