Job Description
Are you ready to architect the future of predictive intelligence? Aether Dynamics is seeking a Senior Data Scientist to lead our core modeling initiatives and transform petabytes of raw data into high-impact strategic insights. In this role, you will work at the intersection of machine learning, statistical inference, and product innovation to solve complex challenges that redefine industry standards.
We offer a high-autonomy environment where you will collaborate with elite engineering teams to deploy production-grade models that drive real-world value. If you thrive on complexity and have a passion for elegant, scalable data solutions, we want to hear from you.
Responsibilities
- Architect, develop, and deploy production-level machine learning models to optimize core business KPIs.
- Lead the design of A/B testing frameworks and complex experimental designs to validate product hypotheses.
- Partner with cross-functional leadership to define the long-term data strategy and roadmap.
- Perform advanced statistical analysis and feature engineering on high-dimensional datasets.
- Mentor junior data scientists and advocate for best practices in code quality and model reproducibility.
- Translate ambiguous business problems into rigorous mathematical frameworks and actionable technical requirements.
- Collaborate with Data Engineering to build robust pipelines and improve data quality across the stack.
Qualifications
- Master’s or PhD in Computer Science, Statistics, Mathematics, or a related quantitative field.
- 5+ years of professional experience as a Data Scientist or Machine Learning Engineer in a high-growth environment.
- Expert-level proficiency in Python (PyTorch, Scikit-Learn, Pandas) and advanced SQL.
- Deep understanding of deep learning, reinforcement learning, or advanced Bayesian statistics.
- Proven track record of deploying scalable ML models into cloud production environments (AWS/GCP).
- Exceptional communication skills with the ability to distill complex findings for non-technical stakeholders.
- Strong experience with Big Data technologies like Spark, Snowflake, or BigQuery.
- Experience with MLOps tools such as MLflow, Kubeflow, or SageMaker.