Senior Data Scientist

Model Building & Training Design, build, and validate machine learning models to predict key business outcomes such as user engagement, content performance (e.g., box office, streaming views), and customer retention in gaming platforms. Utilize supervised and unsupervised learning techniques, including regression models, classification algorithms, clustering, recommendation systems, and time series forecasting, to address business needs. Implement deep learning techniques, including neural networks and natural language processing (NLP) for advanced content analysis and user behavior prediction. Model Deployment & Optimization Deploy machine learning models into production environments, working closely with DevOps or engineering teams to ensure seamless integration into operational workflows. Continuously monitor model performance and refine models based on feedback and evolving data. Optimize models for efficiency, scalability, and accuracy, using advanced techniques like hyperparameter tuning, cross-validation, and model assembling . Data Pipeline Development Collaborate with data engineers to build and maintain efficient data pipelines for preprocessing large-scale datasets. Automate data collection, cleaning, and feature engineering processes to ensure timely and accurate data for model building. Qualifications & Experiences Bachelors degree, MS, or greater in Computer/Data Science, Engineering, Mathematics, Statistics, or related quantitative discipline. 5+ years relevant experience in Data Science . Expertise in a variety of machine learning techniques (clustering, decision tree learning, artificial neural networks , random forests, deep learning etc.) and experience with applications of these techniques . Expertise in SQL and either Python or R, including experience with application deployment packages like R Streamlit or Shiny. Experience with database technologies such as Databricks, Snowflake, and others. Familiarity with BI tools ( Power BI , Looker, Tableau) and experience managing workflows in an Agile environment. High attention to detail and capability to work independently in managing multiple priorities under tight deadlines. Proficiency in big data technologies (e.g., Spark, Kafka, Hive). Experience working in a cloud environment (AWS, Azure, GCP) to facilitate data solutions. Ability to collaborate effectively with business partners and develop and maintain productive professional relationships. Experience with adhering to established data management practices and standards. Interest in movies, games, and comics is a plus .