Lead Data Scientist
Minimum 10+ years of experience in Data Science.
Core Responsibilities
- Strategy & Leadership: Setting the roadmap for data science initiatives, aligning them with business goals, and mentoring junior data scientists.
- Model Development: Overseeing the design, training, and deployment of machine learning models for predictive analytics, recommendation systems, NLP, computer vision, or other domains.
- Data Infrastructure: Collaborating with engineering teams to ensure scalable pipelines, clean data, and efficient model serving.
- Cross-Functional Collaboration: Partnering with product, marketing, and operations teams to translate business problems into data-driven solutions.
- Innovation: Staying ahead of emerging techniques (e.g., generative AI, reinforcement learning, causal inference) and evaluating their potential impact.
Skills & Tools
- Technical: Python, R, SQL, Spark, TensorFlow, PyTorch, cloud platforms (AWS, Azure, GCP).
- Analytical: Strong grounding in statistics, probability, optimization, and experimental design.
- Leadership: Communication, stakeholder management, and the ability to explain complex models in plain language.
- Vision: Identifying opportunities where data science can create competitive advantage.
Minimum 10+ years of experience in Data Science.
Core Responsibilities
- Strategy & Leadership: Setting the roadmap for data science initiatives, aligning them with business goals, and mentoring junior data scientists.
- Model Development: Overseeing the design, training, and deployment of machine learning models for predictive analytics, recommendation systems, NLP, computer vision, or other domains.
- Data Infrastructure: Collaborating with engineering teams to ensure scalable pipelines, clean data, and efficient model serving.
- Cross-Functional Collaboration: Partnering with product, marketing, and operations teams to translate business problems into data-driven solutions.
- Innovation: Staying ahead of emerging techniques (e.g., generative AI, reinforcement learning, causal inference) and evaluating their potential impact.
Skills & Tools
- Technical: Python, R, SQL, Spark, TensorFlow, PyTorch, cloud platforms (AWS, Azure, GCP).
- Analytical: Strong grounding in statistics, probability, optimization, and experimental design.
- Leadership: Communication, stakeholder management, and the ability to explain complex models in plain language.
- Vision: Identifying opportunities where data science can create competitive advantage.