Senior Data Scientist
About this position
You will be responsible for overseeing the collection, storage, and interpretation of data for businesses. If you have strong data analytical skills, join us in processing, modeling, analyzing, and visualizing data for creating tangible business values from insights.
Responsibilities
• Lead new data science initiatives to deliver measurable business value, recommend and experiment with tools, and enhance data science practices.
• Lead end-to-end data science projects, from initial problem definition to deployment, ensuring alignment with business goals.
• Mentor junior data scientists, guiding them on methodologies, technical challenges, and professional development.
• Drive the organization’s data mindset and strategy by promoting best practices and collaborating on impactful analytics projects.
• Design, build, and recommendation systems to enhance customer engagement and deliver tailored experiences.
• Develop and refine predictive models, running experiments to evaluate model performance and implement improvements as needed.
• Leverage advanced analytics, machine learning, and data mining techniques to solve complex business challenges across multiple use cases.
• Apply machine learning, statistical modeling, and data analysis on large, complex datasets to extract meaningful insights and recommend actionable solutions.
• Continuously monitor and enhance analytical models, ensuring accuracy and optimizing for performance and scalability.
• Research and develop new tools, frameworks, and methodologies to expand data science capabilities and drive innovation.
• Stay up-to-date with the latest industry advancements, integrating cutting-edge techniques to keep our data science approaches current and impactful.
Requirements
• Bachelor’s degree or higher in a quantitative field (e.g., Computer Science, Statistics, Mathematics) and 5+ years of experience in data science, AI/ML or related fields.
• Strong programming skills (e.g., Python, R, SQL) and experience with data science libraries (e.g., TensorFlow, PyTorch, Scikit-Learn).
• Proven expertise in one or more areas of advanced analytics and data science, such as machine learning, statistical modeling, recommendation systems, deep learning, natural language processing (NLP), optimization, or algorithm development.
• Familiarity with big data tools (e.g., Hadoop, Spark) and cloud platforms (e.g., AWS, Azure, Google Cloud).