Senior ML Engineer R

Chicago, IL - USA

Monthly Salary: Not Disclosed

Posted on: 27 days ago

Vacancies: 1 Vacancy

Job Summary

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.
Masters 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.

We may use artificial intelligence (AI) tools to support parts of the hiring process such as reviewing applications analyzing resumes or assessing responses. These tools assist our recruitment team but do not replace human judgment. Final hiring decisions are ultimately made by humans. If you would like more information about how your data is processed please contact us.

Required Experience:

Senior IC

Senior Lead AI/ML EngineerPrimary SkillsUnivariate and multivariate time series forecasting modelsSales ForecastingMachine LearningJob requirementsKey Responsibilities Partner with product and business teams to define problems and translate them into data-driven solutions. Conduct exploratory data a...

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.
Masters 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.

Required Experience:

Senior IC

Key Skills

APIs
C/C++
Computer Graphics
Go
React
Redux
Node.js
AWS
Library Services
Assembly
GraphQL
High Voltage

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