
Principal Data Scientist/Architect
2 days ago
Are you a visionary leader in data science and architecture? We are seeking an experienced Principal Data Scientist/Architect to define our data strategy, drive innovation in AI/ML, and build scalable data solutions that impact our business.
Key Responsibilities:- Strategic Leadership: Champion our data science vision and align it with business objectives.
- Data Architecture: Design and architect robust data platforms and solutions to support advanced analytics and AI/ML initiatives.
- Model Development & Innovation: Lead the development and implementation of advanced statistical and machine learning models to solve complex business problems.
- Data Exploration & Analysis: Lead data exploration and analysis efforts, identifying key insights and trends that inform business decisions.
- Mentorship & Team Leadership: Mentor junior data scientists and engineers, fostering a culture of learning and growth.
- Cross-functional Collaboration: Collaborate closely with product managers, engineers, and business stakeholders to understand their needs and translate them into data-driven solutions.
- Research & Development: Stay up-to-date with the latest advancements in data science, AI/ML, and related fields.
- Data Governance & Quality: Champion data governance and quality initiatives, ensuring data accuracy, consistency, and compliance.
Qualifications:
- Education: Ph.D./Bachelor's/Master's degree in Computer Science, Statistics, Mathematics, or a related field.
- Experience: 12+ years of overall experience with minimum 8+ years in data science, with a proven track record of developing and deploying successful AI/ML solutions.
- Technical Skills:
- Strong expertise in statistical modeling, machine learning algorithms (e.g., regression, classification, clustering, deep learning), and data mining techniques.
- Proficiency in programming languages such as Python, R, or similar.
- Experience with big data technologies (e.g., Hadoop, Spark) and cloud computing platforms (e.g., AWS, Azure, GCP).
- Excellent skills in finding central tendency, variability, distribution, probability, discrete and continuous distributions, hypothesis testing, regression analysis, correlation techniques, density estimates, chi-square test, ANOVA technique, A/B Testing, Linear Algebra, Gradient Calculus, N-gram analysis, trend analysis.
- Familiarity with data visualization tools & libraries (e.g., Tableau, Power BI, Plotly, Matplotlib, Seaborn, Vega-Lite).
- Experience with MLOps tools and practices is a plus.
- Dimensionality reduction and feature extraction techniques like Principal Component Analysis, Singular Value Decomposition, and Linear Discriminant Analysis.
- Excellent knowledge of Generative AI (Transformer Models and Generative Adversarial Networks).
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