Lead Data Scientist(W-2)

As a Lead Data Scientist, you will be responsible for leading a team of data scientists and analysts to solve complex business problems using data-driven techniques. You will work closely with cross-functional teams to design, implement, and scale data science models and solutions. Your expertise will influence strategic decisions, drive innovation, and enable data-centric transformations across the organization.

Key Responsibilities:

• Lead the development and deployment of advanced data science models and machine learning algorithms to address business challenges.
• Manage and mentor a team of data scientists, providing guidance and fostering a collaborative environment that encourages innovation.
• Collaborate with stakeholders from different departments (e.g., engineering, marketing, product, and leadership) to understand business needs and deliver actionable insights.
• Drive the adoption of data science best practices, tools, and frameworks across the organization.
• Oversee end-to-end data science workflows, including data collection, feature engineering, model selection, evaluation, and deployment.
• Ensure the scalability and robustness of data science solutions in a production environment.
• Utilize statistical analysis, machine learning, deep learning, and other advanced techniques to drive insights and business outcomes.
• Communicate findings, insights, and recommendations to both technical and non-technical stakeholders through reports, presentations, and dashboards.
• Stay updated with the latest trends in data science and AI, exploring new technologies and methodologies that can enhance the organization’s capabilities.

Qualifications:

• Master’s or Ph.D. in Computer Science, Mathematics, Statistics, Engineering, or a related field.
• 10 years of experience in data science, machine learning, and analytics, with a proven track record of leading teams and delivering impactful solutions.
• Strong proficiency in programming languages such as Python, R, SQL, and other data science and machine learning libraries (e.g., TensorFlow, scikit-learn, PyTorch).
• Extensive experience with machine learning algorithms, statistical modeling, and data mining techniques.
• Proven experience in data wrangling, feature engineering, and working with large, complex datasets.
• Deep understanding of big data technologies and platforms (e.g., Hadoop, Spark, AWS, GCP).
• Solid experience with data visualization tools (e.g., Tableau, Power BI) and techniques to communicate complex insights effectively.
• Strong knowledge of data architectures, cloud infrastructure, and best practices for deploying data science models at scale.
• Excellent leadership, communication, and project management skills.
• Strong business acumen and ability to translate business problems into data science solutions.

Preferred Skills:

• Experience in deploying and maintaining machine learning models in production environments.
• Familiarity with deep learning, natural language processing (NLP), or reinforcement learning.
• Knowledge of Agile methodologies and working in cross-functional teams.
• Experience with cloud platforms (e.g., AWS, GCP, Azure) and containerization (e.g., Docker, Kubernetes).