Machine Learning Engineer

Photon; New Jersey; On-site; USD 150,000 - 190,000; Full time

Job summary

Photon is seeking a seasoned Machine Learning Engineer based in New Jersey to design and deploy analytics-driven models. You will partner with product and engineering teams to deliver end-to-end ML solutions, deploy models into production, and leverage Python, Spark, and Databricks to generate actionable insights.

Qualifications include 5+ years in data analytics, strong Python ML library experience, and proficiency with PySpark and Databricks for large-scale processing.

Qualifications

• Bachelor's or master's degree in CS, Math, Physics, or related field.
• 5+ years of data analytics experience with statistical methods.
• Experience with Python ML libraries (scikit-learn, TensorFlow, PyTorch).
• Experience with Databricks and Spark (PySpark) for large-scale processing.
• Proficient in Pandas and NumPy; data cleaning and preparation.
• Strong problem-solving and communication skills.

Responsibilities

• Analyze use cases and design analytics models using statistical and machine learning methods tailored to specific business needs.
• Develop machine learning algorithms aimed at personalizing customer experiences and extracting actionable business insights.
• Apply data mining and machine learning techniques across forecasting, prediction, segmentation, recommendation, and fraud detection.
• Augment company data by integrating third-party data sources to enhance 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 libraries such as Pandas and NumPy.
• Implement scalable machine learning models with attention to performance, using tools like PySpark in Databricks.
• Design and build infrastructure that supports large-scale data analytics and experimentation.
• Utilize Jupyter Notebooks for data exploration and model development.

Skills

Statistical methods

Python programming

Model deployment

Data analysis

Jupyter notebooks

Education

Bachelor's or Master in CS/Math/Physics

PhD preferred

Tools

Databricks

Spark

PySpark

Pandas

NumPy

scikit-learn

TensorFlow

PyTorch

Job description

Photon is seeking a seasoned Machine Learning Engineer to design and deploy analytics-driven models that solve real business problems. The role is based in New Jersey with onsite collaboration, and you will partner with product and engineering teams to deliver end-to-end ML solutions, deploy models into production, and leverage Python, Spark, and Databricks to generate actionable insights.

Responsibilities

• Analyze use cases and design analytics models using statistical and machine learning methods tailored to specific business needs.
• Develop machine learning algorithms aimed at personalizing customer experiences and extracting actionable business insights.
• Apply data mining and machine learning techniques across forecasting, prediction, segmentation, recommendation, and fraud detection.
• Augment company data by integrating third‑party data sources to enhance 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 libraries such as Pandas and NumPy.
• Implement scalable machine learning models with attention to performance, using tools like PySpark in Databricks.
• Design and build infrastructure that supports large-scale data analytics and experimentation.
• Utilize Jupyter Notebooks for data exploration and model development.

Requirements

• A bachelor's or master's degree in Computer Science, Mathematics, Physics, or related fields; a PhD is preferred but not required.
• Minimum of 5 years of experience in data analytics, with a solid grasp 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 4 continuous years of experience with Spark, particularly using PySpark.
• Hands-on experience with data processing and analysis tools such as Pandas, NumNum?

Technologies

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