What is the role?
The Data Science Lead will play a pivotal role in driving the development and implementation of data science solutions for our product.
Key Responsibilities
- Research and develop advanced statistical and machine learning models for analysis of large-scale, high-dimensional data.
- Dig deeper into data, understand characteristics of data, evaluate alternate models and validate hypothesis through theoretical and empirical approaches.
- Productize proven or working models into production quality code.
- Collaborate with product management, marketing and engineering teams in Business Units to elicit & understand their requirements & challenges and develop potential solutions
- Stay current with latest research and technology ideas; share knowledge by clearly articulating results and ideas to key decision makers.
- Apply knowledge of ML, statistics, and advanced mathematics to conceptualize, experiment and design an intelligent system to augment the existing Analytics system.
- Contributing in development and deployment of AI/ML models for descriptive, predictive and prescriptive analytics.
- Gathering & analyzing data, devising data science solutions for high-performance models in scalable code. Propose innovative algorithms and pursue patents where appropriate
- Researching and evaluating emerging technology and market trends to assist in project development and operational support for multiple teams or complex scenarios.
Requirements & Skills
- Bachelor’s degree in Computer Science, Statistics, Mathematics, or a related field; Master’s degree preferred.
- 7+ years of experience in data science, machine learning, or related roles, with a track record of successfully leading and delivering data science projects in a SaaS product environment.
- Expertise in machine learning algorithms, statistical analysis, and data mining techniques, with hands-on experience in model development, evaluation, and deployment.
- Proficiency in programming languages such as Python, R, or Scala, as well as data science libraries and frameworks such as TensorFlow, PyTorch, scikit-learn, or Spark MLlib.
- Experience with cloud-based data platforms and services, such as AWS, Google Cloud Platform, or Microsoft Azure, for building and deploying scalable data science solutions.
- Strong leadership and team management skills, with the ability to inspire, motivate, and mentor a team of data scientists to achieve their full potential.
- Excellent communication and collaboration skills, with the ability to effectively communicate complex technical concepts to non-technical stakeholders and influence decision-making.
- Proven track record of delivering data-driven solutions that drive business value and impact, with a focus on innovation, scalability, and performance.