Job
Description
Research and Problem-Solving: Identify and frame business problems, conduct exploratory data analysis, and propose innovative data science solutions tailored to business needs. Leadership & Communication: Serve as a technical referent for the research team, driving high-impact, high-visibility initiatives. Effectively communicate complex scientific concepts to senior stakeholders, ensuring insights are actionable for both technical and non-technical audiences. Mentor and develop scientists within the team, fostering growth and technical excellence. Algorithm Development: Design, optimize, and implement advanced machine learning algorithms, including neural networks, ensemble models (XGBoost, random forests), and clustering techniques. End-to-End Project Ownership: Lead the development, deployment, and monitoring of machine learning models and data pipelines for large-scale applications. Model Optimization and Scalability: Focus on optimizing algorithms for performance and scalability, ensuring robust, well-calibrated models suitable for real-time environments. A/B Testing and Validation: Design and execute experiments, including A/B testing, to validate model effectiveness and business impat. Big Data Handling: Leverage tools like BigQuery, advanced SQL, and cloud platforms (e.g., GCP) to process and analyze large datasets. Collaboration and Mentorship: Work closely with engineering, product, and campaign management teams, while mentoring junior data scientists in best practices and advanced techniques. Data Visualization: Create impactful visualizations using tools like matplotlib, seaborn, Looker, and Grafana to communicate insights effectively to stakeholders. Required Experience/Skills 5–8 years of hands-on experience in data science or machine learning roles. 2+ years leading data science projects in AdTech Strong hands-on skills in Advanced Statistics, Machine Learning, and Deep Learning. Demonstrated ability to implement and optimize neural networks and other advanced ML models. Proficiency in Python for developing machine learning models, with a strong grasp of TensorFlow or PyTorch. Expertise handling large datasets using advanced SQL and big data tools like BigQuery In-depth knowledge of MLOps pipelines, from data preprocessing to deployment and monitoring. Strong background in A/B testing, statistical analysis, and experimental design. Proven capability in clustering, segmentation, and unsupervised learning methods. Strong problem-solving and analytical skills with a focus on delivering business value. Education: A Master’s in Data Science, Computer Science, Mathematics, Statistics, or a related field is preferred. A Bachelor's degree with exceptional experience will also be considered.
