Southern Arkansas University
Machine Learning Engineer
Southern Arkansas University, Snowflake, Arizona, United States, 85937
A well-funded, fast-growing data-driven company based in Chicago is seeking a
Machine Learning Engineer
to spearhead its Marketing Intelligence initiatives. This is an exciting opportunity to lead the development of production-grade ML models, collaborate with business stakeholders, and drive insights that impact real-world decisions. The ideal candidate has a deep understanding of machine learning pipelines, data engineering, and a proven track record of scaling models in cloud environments. Role:
Design, build, and fine-tune ML models to solve complex business problems Build scalable, cloud-agnostic ML pipelines and workflows Apply state-of-the-art techniques in deep learning, time-series forecasting, and NLP Integrate model outputs into Marketing Intelligence systems and dashboards Collaborate with business leaders to align model outputs with key objectives Conduct experiments and analyze data to extract actionable insights Document findings and communicate effectively across technical and non-technical teams Requirements:
Master’s degree in a quantitative field (preferred) 7+ years of experience in data science and engineering Strong foundation in statistical modeling, regression, classification, clustering, and causal inference Expertise in marketing analytics (MMM, Attribution, Propensity Models) Proficiency in Python, R, and SQL Familiarity with MLOps tools and platforms (AWS SageMaker, Google Vertex AI, Azure ML) Hands-on with frameworks like Scikit-learn, XGBoost, TensorFlow, and PyTorch Strong communication skills and a collaborative mindset Benefits:
Salary range:
$150,000 – $180,000/year 401(k) with 75% match up to 7% Equity/Stock Ownership potential Comprehensive health and wellness support Hybrid work environment (3 days/week in Chicago office) Skills:
machine learning, data science, deep learning, PyTorch, TensorFlow, Python, R, SQL, MLOps, Airflow, Spark, PySpark, marketing analytics, attribution modeling, MMM, Snowflake, BigQuery, Databricks, regression, classification, causal inference
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Machine Learning Engineer
to spearhead its Marketing Intelligence initiatives. This is an exciting opportunity to lead the development of production-grade ML models, collaborate with business stakeholders, and drive insights that impact real-world decisions. The ideal candidate has a deep understanding of machine learning pipelines, data engineering, and a proven track record of scaling models in cloud environments. Role:
Design, build, and fine-tune ML models to solve complex business problems Build scalable, cloud-agnostic ML pipelines and workflows Apply state-of-the-art techniques in deep learning, time-series forecasting, and NLP Integrate model outputs into Marketing Intelligence systems and dashboards Collaborate with business leaders to align model outputs with key objectives Conduct experiments and analyze data to extract actionable insights Document findings and communicate effectively across technical and non-technical teams Requirements:
Master’s degree in a quantitative field (preferred) 7+ years of experience in data science and engineering Strong foundation in statistical modeling, regression, classification, clustering, and causal inference Expertise in marketing analytics (MMM, Attribution, Propensity Models) Proficiency in Python, R, and SQL Familiarity with MLOps tools and platforms (AWS SageMaker, Google Vertex AI, Azure ML) Hands-on with frameworks like Scikit-learn, XGBoost, TensorFlow, and PyTorch Strong communication skills and a collaborative mindset Benefits:
Salary range:
$150,000 – $180,000/year 401(k) with 75% match up to 7% Equity/Stock Ownership potential Comprehensive health and wellness support Hybrid work environment (3 days/week in Chicago office) Skills:
machine learning, data science, deep learning, PyTorch, TensorFlow, Python, R, SQL, MLOps, Airflow, Spark, PySpark, marketing analytics, attribution modeling, MMM, Snowflake, BigQuery, Databricks, regression, classification, causal inference
#J-18808-Ljbffr