Experience Range: 5 - 8 years of experience, including at least 5 years of hands-on work in data science, analytics, or related fields, with recent exposure to agentic AI solutions Key Responsibilities:
Translate complex business challenges into structured data science problems, ensuring alignment with organizational objectives and measurable outcomes
Develop, monitor, and validate OKRs using advanced statistical techniques to deliver actionable insights and track progress
Execute advanced data wrangling, cleansing, and transformation on large, complex datasets to enable robust modeling and analysis
Deliver impactful data-driven insights through clear data storytelling, utilizing visualization tools to communicate findings effectively to stakeholders
Apply design thinking methodologies to create innovative analytical solutions and continuously optimize data science workflows and processes
Lead technical decision-making for modeling iterations, optimizing model performance, and balancing computational efficiency with business requirements
Collaborate with cross-functional teams, including engineering and product, to implement scalable data science solutions that drive business value
Promote data literacy and foster a culture of data-driven decision-making by sharing best practices and industry trends across the organization
Required Skills:
Advanced proficiency in Python or R for data wrangling, preprocessing, and statistical analysis
Expertise in statistical modeling and validation of performance metrics
Experience with data visualization tools such as Tableau, Power BI, or Matplotlib
Hands-on experience with machine learning algorithms and evaluation metrics
Strong background in feature engineering and data mining
Familiarity with big data technologies such as Spark or Hadoop
Experience with cloud-based data platforms including AWS, Azure, or Google Cloud
Knowledge of MLOps practices and deployment pipelines
Preferred Skills:
Experience with deep learning frameworks such as TensorFlow or PyTorch
Exposure to agentic AI and rapid domain adaptation
Experience with automation tools and scripting for data workflows
Desired Qualifications:
Bachelor's degree in Computer Science, Statistics, Mathematics, Data Science, or a closely related discipline
Certification in Data Science or Machine Learning from a recognized institution, such as Microsoft Certified: Azure Data Scientist Associate or IBM Data Science Professional Certificate
Relevant coursework or certification in statistical analysis or business analytics
