Data Scientist

Amtex Systems Inc; Parsippany-Troy Hills (NJ); On-site; USD 150,000 - 190,000; Full time

Job summary

Amtex Systems Inc. in Parsippany‑Troy Hills, NJ, seeks a senior ML engineer to design and optimize recommendation systems for consumer marketing. You will build personalization models, scale them for real‑time predictions, and drive engagement and revenue growth.

You will collaborate with marketing, product, and engineering teams to productionize models, analyze performance with CTR, conversion, and CLV metrics, and explore advanced techniques like RL and graph neural networks.

Qualifications

• 5+ years of experience in machine learning with a focus on recommendation systems.
• Proven track record delivering personalized recommendations that improve engagement.
• Experience with consumer marketing data (behavioral, transactional, campaign).

Responsibilities

• Design and develop ML models for recommendation and personalization systems tailored to consumer marketing use cases.
• Optimize models for scalability, performance, and real-time predictions on large datasets.
• Collaborate with marketing, product, and engineering to productionize models in campaigns.
• Analyze and improve recommendation quality using relevant metrics.
• Leverage customer segmentation and first‑party data to enhance personalization.
• Experiment with RL, graph neural networks, and contextual bandits to improve outcomes.

Skills

Recommendation systems

Machine learning

Python

TensorFlow

PyTorch

Collaborative filtering

Deep learning

Rankings

Reinforcement learning

Graph neural networks

Tools

Databricks

AWS

CDPs

Job description

Recommendation Systems (Consumer Marketing)

We are seeking a skilled deep expertise in building and optimizing recommendation systems within the consumer marketing space. The ideal candidate will have hands‑on experience designing, implementing, and scaling personalized recommendation and targeting models that drive customer engagement, conversion, and revenue growth. Experience translating consumer behavior and marketing data into actionable, personalized experiences is essential.

Key Responsibilities:

• Design and develop machine learning models for recommendation and personalization systems (e.g., collaborative filtering, deep learning, hybrid approaches) tailored to consumer marketing use cases such as product recommendations, next‑best‑action, and audience targeting.
• Optimize models for scalability, performance, and real‑time predictions across large‑scale consumer datasets.
• Collaborate with business leaders, marketing partners, product and engineering teams to integrate models into production and campaign pipelines.
• Analyze and improve recommendation quality using metrics like precision, recall, click‑through rate, conversion, and customer lifetime value.
• Leverage customer segmentation, behavioral, and first‑party marketing data to enhance personalization and relevance.
• Experiment with cutting‑edge techniques (e.g., reinforcement learning, graph neural networks, contextual bandits) to enhance recommendations and marketing outcomes.

Requirements:

• 5+ years of experience in machine learning, with a focus on recommendation systems, ideally within consumer marketing, retail, e-commerce, or a related consumer‑facing domain.
• Proven track record building personalization or recommendation models that measurably improved engagement or marketing performance.
• Proficiency in Python, TensorFlow, PyTorch, or similar ML frameworks.
• Strong understanding of algorithms like matrix factorization, neural networks, and ranking systems.
• Strong understanding of LTMs and agentic AI frameworks that can be customized for recommender systems
• Experience working with consumer/marketing data, including behavioral, transactional, and campaign data (familiarity with CDPs, marketing analytics, or A/B testing is a plus).
• Experience with Databricks and AWS.
• Excellent problem‑solving skills and a passion for delivering impactful, customer‑centric solutions.