Lead Data Scientist

About You

As a Lead Data Scientist, you will drive our data science initiatives from concept to impactful delivery. You will play a crucial role in driving data-informed decisions across the organization, leading team members to ensure we deliver projects with actual business impact, and championing technical excellence and innovation within our data science team. Your Day-To-Day

Initiating and Leading Projects : Identifying opportunities where data science can add value and solve business problems, including prescriptive analytics and predictive modeling. Managing the entire project lifecycle from solution design to model deployment, including task prioritization and risk mitigation, to ensure the timely delivery of high-quality data products and insights. Driving Impactful Outcomes : Ensuring data science contributions directly support and advance business objectives, focusing on improving operational efficiency, optimizing product offerings, and identifying new growth opportunities. Ensuring Strategic Alignment : Effectively communicating complex data findings and recommendations to both technical and non-technical stakeholders, ensuring insights are clearly understood and directly address strategic business needs. Guiding Team on Methodologies : Establishing and championing best practices, frameworks, and robust internal processes for data science methodologies to keep the team at the forefront of the field. Overseeing Delivery & Production Maintenance : Being responsible for the successful delivery and ongoing maintenance of machine learning, data science, and AI initiatives, including monitoring model performance and addressing data drifts. Your Know-How

Extensive Experience : 6+ years of experience in data science. Preference will be given to those who have a proven track record of leading and mentoring junior data scientists. Programming Proficiency : Expert-level proficiency in Python and / or R, including experience with relevant data science libraries (e.g., NumPy, Pandas, Scikit-learn, TensorFlow, Keras, PyTorch). Machine Learning / AI Expertise : Deep understanding and practical experience with a wide range of machine learning algorithms (e.g., supervised, unsupervised, reinforcement learning, deep learning) and their applications. Data Warehousing & Databases : Proficient in SQL and experience working with large-scale databases and data warehousing solutions (e.g., Snowflake, BigQuery, Redshift). Cloud Platforms : Hands-on experience with cloud platforms (e.g., AWS, Azure, GCP) for data storage, processing, and model deployment. MLOps : Familiarity with MLOps principles and tools for deploying, monitoring, and maintaining machine learning models in production. Communication & Storytelling : Exceptional ability to translate complex analytical findings into clear, concise, and actionable insights for diverse audiences.

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