Senior Data Scientist

Senior Data Scientist

Requirements

• Bachelor’s Degree in Information Technology or related field required.
• Master’s or Ph.D. in Computer Science, Statistics, Mathematics, or a related field preferred
• 5+ years of relevant experience required.
• Expertise in Python and proficiency in ML frameworks (TensorFlow, PyTorch, scikit-learn).
• Deep understanding of ML algorithms (supervised, unsupervised learning, and deep learning) and their applications.
• Strong problem-solving, critical thinking, and analytical capabilities.

Responsibilities

• Leads multiple data science projects ensuring alignment with business goals.
• Develops predictive models and integrates them with Business Intelligence tools.
• Develops and maintains data pipelines for efficient data retrieval and processing. Collaborates with applications and data engineering teams for deploying models at scale.
• Mentors junior data scientists in model development and data handling.
• Engages with Senior Leadership to inform strategic decisions using business intelligence insights.
• Researches and adopts cutting-edge technologies and methodologies in data science.
• Manages stakeholder expectations and delivers actionable solutions.
• Oversees data processing pipelines ensuring data quality and consistency.
• Drives ethical considerations in model deployment and data utilization.
• Collaborates with external partners, research institutions, and subject matter experts to gather domain-specific knowledge and datasets.
• Performs exploratory data analysis to identify patterns, insights, and communicate findings.
• Engage in the ideation and prototyping of new solutions to meet emerging business requirements.
• Utilize advanced machine learning techniques (e.g., deep learning, NLP, computer vision, reinforcement learning) to create innovative solutions.

Preferred Qualifications

• Artificial Intelligence (AI) and Machine Learning (ML) – Understanding of AI/ML concepts, algorithms, and platforms to design architectures that support intelligent systems and enable AI-driven applications.
• Business Domain Knowledge – Understanding of business processes, industry trends, and market dynamics to provide relevant and actionable insights for strategic decision-making.
• Communication and Collaboration – Excellent communication skills to effectively interact with stakeholders, gather requirements, present architectural proposals, and collaborate with cross-functional teams.
• Data Analysis – The process of measuring and managing organizational data, identifying methodological best practices, and conducting statistical analyses.
• Data Ethics & Responsible Innovation – Knowledge of ethical considerations related to data usage, data-driven technologies, and strategies to mitigate biases in data-driven decision-making.
• Data Mining and Extraction – Data mining is sorting through data to identify patterns and establish relationships.
• Data Monetization and Data Science – Familiarity with data monetization strategies and techniques, such as data commercialization, data marketplaces, and data value realization.
• Natural Language Processing – Proficiency in analyzing and extracting insights from unstructured text data, including sentiment analysis, topic modeling, and language understanding.
• Problem-Solving and Analytical Thinking – Strong problem-solving skills to identify architectural challenges, analyze requirements, evaluate options, and propose effective solutions.
• Reporting and Dashboarding – The ability to access information from databases, forms, and other sources, and prepare reports according to requirements.
• Statistical Analysis – Statistical Analysis is used in support of decision-making and includes fundamental principles such as data collection and sampling, random variable types and probability distributions, sampling, and population distributions, making estimations from samples, hypothesis testing, and statistical process control.