Job Description
Join DataFlow Analytics as a Senior Data Scientist to architect transformative solutions using cutting-edge AI and machine learning. We're seeking a visionary leader to drive innovation in predictive analytics, natural language processing, and deep learning while mentoring a high-impact team. You'll collaborate with cross-functional stakeholders to convert complex business challenges into data-driven strategies that accelerate growth.
We offer competitive compensation, flexible work arrangements, and opportunities to publish research at premier conferences. Our startup culture values creativity, intellectual curiosity, and technical excellence.
Responsibilities
- Design and implement scalable ML pipelines for real-time data processing and model deployment
- Lead end-to-end data science projects from hypothesis to production using Python, TensorFlow, and cloud infrastructure
- Develop innovative algorithms for predictive analytics, NLP, and computer vision applications
- Translate business requirements into technical specifications and communicate findings to executive stakeholders
- Mentor junior data scientists and establish best practices for model validation and ethical AI
- Conduct A/B testing and statistical analysis to drive product optimization
- Collaborate with engineering teams to integrate models into production systems
Qualifications
- PhD or Master's in Computer Science, Statistics, Mathematics, or related field with 5+ years industry experience
- Expert proficiency in Python (scikit-learn, pandas) and ML frameworks (TensorFlow, PyTorch)
- Strong foundation in statistical modeling, experimental design, and hypothesis testing
- Proven experience deploying production ML systems using cloud platforms (AWS/GCP/Azure)
- Exceptional communication skills with ability to articulate complex concepts to non-technical audiences
- Portfolio demonstrating published research or open-source contributions
- Experience with big data technologies (Spark, Hadoop) and SQL/NoSQL databases
- Knowledge of MLOps tools (Kubernetes, Kubeflow) and CI/CD practices