Position Description
As a Senior Data Scientist at Cinemo, you will drive data-driven decision-making through cross-functional collaboration on several topics. This role requires a deep understanding of statistics, machine learning, and the ability to communicate results and insights to a range of audiences. You will play a critical role in analyzing complex datasets, developing scalable data pipelines, and applying Bayesian approaches to extract actionable insights.
This role will work closely with engineers, product managers, and business leaders to improve AI models, optimize product features, and enhance organizational data capabilities. If you have a passion for solving challenging problems at the intersection of data science, AI, and customer engagement, we’d love to hear from you!
In this role, you will:
- Develop and optimize ETL pipelines, ensuring high-quality, reliable data
- Design and conduct statistical studies and data analysis to evaluate the impact of internally adopted AI tools, research, and engineering results and to create interpretable insights and make data-driven decisions
- Curate and maintain datasets to support the development, evaluation, and deployment of AI models
- Provide technical leadership, mentorship, and guidance to the AI team and internal research projects, fostering a culture of innovation and excellence
- Partner with machine learning engineers, product managers, and executives to translate data insights into tangible business and product improvements
- Develop scalable algorithms and automated data processing frameworks to optimize analytics workflows
What you will need to succeed:
- PhD or MS in Computer Science, Data Science, Statistics or a related quantitative field with scientific background and with 5+ years of relevant experience
- Strong expertise in data science, Bayesian modeling, probabilistic programming, and uncertainty quantification
- Hands-on experience with neural network analysis, deep learning frameworks (e.g., TensorFlow, PyTorch), and model evaluation
- Proficiency in Python, R, SQL, and data engineering tools such as Spark or Apache Beam and experience in designing, executing, and analyzing A/B tests
- Ability to develop and optimize ETL pipelines for large-scale data processing
- Solid understanding of causal inference, time series forecasting, and statistical modeling
- Hands-on experience with cloud computing platforms (e.g., AWS, GCP, Azure) and big data tools
- Knowledge in natural language processing (NLP), reinforcement learning, and graph analytics is preferable
