TechStar Group
ML Engineer with Timeseries data experience
TechStar Group, Atlanta, Georgia, United States, 30383
ML Engineer with Timeseries Data Experience
Location:
Hybrid in Atlanta, GA (locals preferred) |
Compensation:
$58/hr on C2C, Any Visa |
Employment Type:
Full-time
Seniority Level:
Mid-Senior level
We are looking for an experienced Machine Learning Engineer with expertise in time‑series data to join our team. This role involves designing, building, and deploying scalable ML models for forecasting, anomaly detection, and predictive analytics.
Responsibilities
Design, build, train, and optimize ML/DL models for time‑series forecasting, prediction, anomaly detection, and causal inference.
Create robust data pipelines for collection, preprocessing, feature engineering, and labeling of large‑scale time‑series data.
Architect and implement scalable AI/ML infrastructure and MLOps pipelines (CI/CD, monitoring) for production deployment.
Collaborate with data engineers, software developers, and domain experts to integrate AI solutions.
Monitor, troubleshoot, and optimize model performance, ensuring robustness and real‑world applicability.
Qualifications
Good understanding of AWS, Python (Pandas, NumPy), PyTorch, TensorFlow, Scikit‑learn, PySpark.
Strong grasp of time‑series models (ARIMA, Prophet, Deep Learning), anomaly detection, and predictive analytics.
Experience with large datasets, feature engineering, and scalable data processing.
Benefits Referrals increase your chances of interviewing by 2x. Additional benefits may be discussed during the interview process.
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Hybrid in Atlanta, GA (locals preferred) |
Compensation:
$58/hr on C2C, Any Visa |
Employment Type:
Full-time
Seniority Level:
Mid-Senior level
We are looking for an experienced Machine Learning Engineer with expertise in time‑series data to join our team. This role involves designing, building, and deploying scalable ML models for forecasting, anomaly detection, and predictive analytics.
Responsibilities
Design, build, train, and optimize ML/DL models for time‑series forecasting, prediction, anomaly detection, and causal inference.
Create robust data pipelines for collection, preprocessing, feature engineering, and labeling of large‑scale time‑series data.
Architect and implement scalable AI/ML infrastructure and MLOps pipelines (CI/CD, monitoring) for production deployment.
Collaborate with data engineers, software developers, and domain experts to integrate AI solutions.
Monitor, troubleshoot, and optimize model performance, ensuring robustness and real‑world applicability.
Qualifications
Good understanding of AWS, Python (Pandas, NumPy), PyTorch, TensorFlow, Scikit‑learn, PySpark.
Strong grasp of time‑series models (ARIMA, Prophet, Deep Learning), anomaly detection, and predictive analytics.
Experience with large datasets, feature engineering, and scalable data processing.
Benefits Referrals increase your chances of interviewing by 2x. Additional benefits may be discussed during the interview process.
#J-18808-Ljbffr