We are looking for a highly skilled Recommendation Systems Specialist to join our data science team. In this role, you will design, develop, and optimize personalized recommendation systems that enhance user experiences and drive business growth. You will work with large datasets, employ machine learning and data mining techniques, and leverage modern algorithms to create tailored recommendations for users across various platforms and services.
The ideal candidate will have a strong background in machine learning, data analysis, and recommender system techniques, as well as a passion for building systems that improve customer engagement and satisfaction.
Key Responsibilities:
Design and Build Recommendation Systems:
Develop personalized recommendation algorithms using collaborative filtering, content-based filtering, hybrid methods, and deep learning techniques.
Design scalable, efficient systems that can process large amounts of user interaction data in real time or near real time.
Experiment with different algorithms (e.g., matrix factorization, nearest neighbor, neural networks) to improve the quality of recommendations.
Data Analysis & Feature Engineering:
Analyze large-scale user data, transaction histories, and behavioral patterns to extract meaningful features for model development.
Implement techniques for handling cold-start problems, incorporating contextual information (e.g., user demographics, preferences), and leveraging implicit and explicit feedback.
Preprocess and clean datasets for model training, ensuring high-quality, relevant data.
Optimization & Performance Tuning:
Continuously improve recommendation algorithms through experimentation, A/B testing, and model evaluation.
Optimize models for scalability, speed, and accuracy, ensuring they can handle high volumes of data and deliver timely recommendations.
Monitor model performance and implement retraining strategies to keep the system up to date with evolving user behavior.
Collaboration & Communication:
Collaborate with data engineers, product managers, and business stakeholders to understand requirements and ensure the recommendations align with business goals.
Present findings, insights, and recommendations clearly to non-technical stakeholders.
Work with engineering teams to deploy models into production environments and integrate them with the platform’s front-end systems.
Continuous Learning and Research:
Stay up to date with the latest developments in recommendation algorithms, machine learning, and artificial intelligence.
Research and evaluate new techniques to improve recommendation quality, such as reinforcement learning, multi-arm bandit models, or hybrid recommender systems.
Contribute to the development of best practices and guidelines for building recommendation systems at scale.
Required Skills & Qualifications:
Education:
Bachelor’s or Master’s degree in Computer Science, Data Science, Machine Learning, or a related field (Ph.D. is a plus).
Experience:
3+ years of experience in designing, developing, and deploying recommendation systems in production environments.
Strong experience with machine learning frameworks and libraries such as TensorFlow, PyTorch, Scikit-learn, or similar.
Proven experience in recommender system algorithms such as collaborative filtering, matrix factorization, k-nearest neighbors (k-NN), content-based filtering, and hybrid methods.
Familiarity with time-series analysis and sequential recommendation models (e.g., RNN, LSTM).
Strong programming skills in Python, Java, or similar languages, with experience in working with big data frameworks (e.g., Hadoop, Spark, etc.).
Technical Skills:
Advanced knowledge of machine learning, statistics, and data analysis techniques.
Expertise in data wrangling and feature engineering for recommender systems.
Experience with recommendation evaluation metrics (e.g., precision, recall, diversity, novelty, and ranking metrics like NDCG).
Proficiency with SQL and NoSQL databases (e.g., PostgreSQL, MongoDB) and data querying.
Experience with cloud platforms and big data tools (e.g., AWS, Google Cloud, Hadoop, Spark).
Soft Skills:
Strong problem-solving abilities and analytical thinking.
Excellent communication skills with the ability to explain complex algorithms and results to non-technical stakeholders.
Ability to work collaboratively in a cross-functional team, managing multiple projects and priorities.
Proactive, self-motivated, and eager to learn and experiment with new technologies.
Preferred Skills:
Experience with deploying recommendation systems at scale in production environments.
Knowledge of deep learning techniques for recommendation, such as neural collaborative filtering (NCF) or reinforcement learning.
Experience with real-time recommendation systems and managing data pipelines.
Knowledge of recommender system frameworks or tools (e.g., Surprise, LightFM, RecBole).
Familiarity with natural language processing (NLP) techniques, especially for content-based recommendations.
Experience with A/B testing, experimentation frameworks, and optimization techniques in production.
Working Environment:
Flexible working hours and remote work options available.
Collaborative and innovative team culture, fostering a spirit of experimentation and learning.
Opportunities for continuous learning and development in AI and machine learning.
Competitive compensation package with benefits such as health insurance, retirement plans, and bonuses.
