Sales - Machine Learning Engineer, Data Model

Key Skills

Data Modeling, Effective communication skills, Forecasting, Keras software library, Machine learning techniques, PyTorch, Sales, Scikit-learn, SPARK Programming, SQL, TensorFlow

Job Description

In this role, you will collaborate with a multidisciplinary team of ML engineers, data scientists, software engineers, researchers, designers and business partners to design, build, and deploy high-impact models.

You will focus on the following key areas:

• Deploy predictive models to generate actionable insights for business strategy and decision-making.

• Develop AI-driven personalization that provide tailored suggestions based on customer behavior, preferences, and historical data.

• Leverage user segmentation and clustering to enhance personalization precision for different customer groups.

• Experiment with multi-modal data (text, images, customer interactions) to improve personalization.

• Implement hybrid personalization models (Collaborative Filtering, Content-Based, Knowledge Graphs) to optimize user experiences.

• Build real-time personalization pipelines that can dynamically adjust based on live user interactions.

• Lead the exploration for predictive modeling of Large Language Models and Generative AI, Causal Inference Model, GNN, venturing into new areas within these fields.

• Turn prototypes into automated pipelines and deploying them to production; deciding when to use out-of-the-box solutions vs. building custom solutions or a hybrid approach.

• Analyze and preprocess large scale datasets to extract meaningful patterns and ensure model accuracy.

• Ongoing data analysis to build new or fine-tune existing models to optimize results.

• Partner closely with software engineers to implement these models into high-performing systems and models in our production environment that can be applied to create amazing experience for our worldwide audience.

• Actively engaging in all aspects of model development, from ideation, experimentation, triaging to deployment.

• Communicate results/reports with stakeholders.

• Maintain expertise in the latest advancements in AI technology. Partnering with your team members to prepare presentations, papers, and patents for your inventions.

Minimum Qualifications

• 5+ years of professional experience in building and deploying predictive models and AI-driven personalization at scale.
• Ph,D. in Computer Science, Artificial Intelligence, Machine Learning or related field; or
• M.S. in related field with 3+ years experience applying machine learning engineer to real business problems.

Preferred Qualifications

• Proven expertise in data preprocessing, feature engineering, and analyzing large datasets to extract meaningful patterns.
• Strong knowledge of state-of-the-art ML algorithms, including Generative AI, Multi-modal LLMs.
• Solid understanding of insight modeling (Causal Inference Model, GNN, Generative AI, Forecasting).
• Hands-on experience in forecasting models, anomaly detection, and AI-driven personalization (matrix factorization, contextual recommendation, collaborative filtering).
• Proficiency in Python and key ML frameworks (TensorFlow, PyTorch, Keras, scikit-learn).
• Experience working with Big Data tools (SQL, Spark, Hadoop) and cloud-based ML pipelines.
• Track record of deploying ML models into production and optimizing for performance and scalability.
• Excellent communication and soft skills.
• Strong portfolio of shipped ML products, patents, or published research is a plus.