Statistical Modeling Developer Jobs

1 job found

1d ago

Machine Learning Engineer

Photon New Jersey

New Jersey

1d ago

Full time Python Advanced Analytics Ai Ml

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Job Description

Photon seeks a skilled Machine Learning Engineer for an onsite role in New Jersey. The position focuses on designing and deploying analytics models through statistical and machine learning approaches, collaborating with product and engineering teams to solve complex business problems, and delivering end-to-end ML solutions in production using Python, Spark, and Databricks.

Responsibilities

Assess business use cases and design analytics models using statistical and machine learning methods tailored to specific requirements.
Develop machine learning algorithms to personalize customer experiences and generate actionable business insights.
Apply data mining and ML techniques, including forecasting, prediction, segmentation, recommendation, and fraud detection.
Augment company data with third-party sources to broaden analytics capabilities.
Improve data collection processes to capture information essential for analytics systems.
Prepare raw data for analysis, including cleaning, imputing missing values, and standardizing formats using Python data frameworks (Pandas, NumPy).
Implement ML models with attention to performance and scalability using PySpark in Databricks.
Design and build infrastructure to support large-scale data analytics and experimentation.
Utilize tools such as Jupyter Notebooks for data exploration and model development.

Requirements

Bachelor's or graduate degree in Computer Science, Mathematics, Physics, or related fields; PhD preferred but not required.
Minimum five years of experience in data analytics with a strong understanding of core statistical algorithms, including classification and regression.
Strong experience with Python-based machine learning libraries such as scikit-learn, TensorFlow, and PyTorch.
Proficiency with analytics platforms like Databricks for large-scale data processing.
At least four years of continuous experience with Spark, particularly PySpark.
Hands-on experience with data processing and analysis tools including Pandas, NumPy, and Jupyter Notebooks.

Technologies

Python
Spark
Databricks
Pandas
NumPy
Jupyter Notebooks
PySpark
scikit-learn
TensorFlow
PyTorch

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