Senior Data Scientist

About The SDS™ Exam

The SDS™ certification exam is built on the DASCA Essential Knowledge Framework (DASCA-EKF™) — a globally benchmarked standard outlining the core competencies, tools, and practices required of senior data science professionals.

The exam assesses readiness across five key domains: data science applications, foundational methods, big data ecosystems, advanced and generative AI, and MLOps. Each domain reflects the evolving demands of senior data roles where translating analytics into business impact, ensuring model integrity, and scaling solutions are as critical as technical proficiency.

Designed to validate both conceptual mastery and applied understanding, the exam goes beyond tool-specific knowledge to evaluate strategic thinking, cross-functional fluency, and ethical leadership in real-world data environments. Weightings across domains are carefully calibrated to reflect the broadening scope of senior data science responsibilities from foundational fluency to domain-driven innovation and operational design.

The SDS™ exam emphasizes:

• Applied understanding of how data science drives enterprise decision-making and ROI.
• Conceptual fluency in tools, frameworks, and methods—beyond platform-specific use.
• Exposure to current and emerging topics, including Generative AI, Responsible AI, and MLOps.
• Strategic alignment—the ability to contextualize technical work within business objectives and impact models.

Examination Coverage Information

• Data Science Applications and Driving Business Through Analytics

  25%

  Real-world applications highlight data science’s direct business impact, inspiring junior practitioners with practical examples and guiding senior data scientists toward domain-specific strategies and cross-department collaboration for ROI. Beyond model-building, success hinges on tangible value—like improved decision-making, cost savings, or revenue growth. Juniors see how analytics shapes strategic direction, while seniors drive large-scale transformations, executive buy-in, and measurable frameworks.

  Key Topics & Use Cases:

  • Industry Use Cases: Healthcare (diagnostics), Finance (risk scoring), Retail (marketing, personalization)
  • Advanced NLP Applications: Chatbots, sentiment analysis, text summarization
  • Computer Vision: Object detection, image recognition
  • Recommendation Systems: Personalization and user profiling
  • IoT & Real-Time Analytics: Sensor data, device-level intelligence
  • Data-Driven Decision Making: Tying analytics to ROI, cost-benefit analysis
  • Operationalizing ML: Scaling AI solutions, embedding into enterprise systems
  • Monetization of AI: Pricing models, licensing, data products
  • Leadership & Change Management: Ensuring adoption of data-driven insights

• Data Science Essentials

  30%

  This section builds the core skill set every data scientist must master, especially those new to the field. Mastering fundamentals ensures consistent, high-quality analyses and lays the groundwork for more advanced topics. Senior roles typically spend less time on the “basics” but must still ensure their teams excel here.

  Key Topics & Use Cases:

  • Programming & Tooling: Python, GithubNotebook Environments (Google Colab)
  • Exploratory Data Analysis (EDA): Visualization, Data Profiling
  • Foundations: Statistics, Probability, Linear Algebra, Basic ML (Classification/Regression/Clustering)
  • Feature Engineering: Basic pipelines, transformations
  • Data Storytelling: Presentations, dashboards, effective communication

• Big Data & Cloud Platforms

  15%

  As data volumes grow, the ability to process and manage “big data” in distributed, cloud-native environments is essential. Both junior and senior data scientists must understand the ecosystem to ensure scalability, efficient resource usage, and integration with enterprise architectures.

  Key Topics & Use Cases:

  • Modern Data Architectures: Data Lakes vs. Data Lakehouses, Cloud Storage (AWS S3, Azure Data Lake, GCP Storage)
  • Real-Time Data Processing: Kafka, Spark Streaming, Flink
  • Distributed Computing: Spark, Dask
  • Vector Databases: Storing embeddings for NLP/CV tasks
  • Data Governance & Quality: Cataloging, lineage, data modeling, master data management (MDM)

• Advanced Data Science & Generative AI

  15%
  • Provides insight into advanced methodologies, promoting continuous learning
  • Junior data scientists gain exposure to the development of sophisticated solutions; senior staff spearhead strategic innovation

  Key Topics & Use Cases:

  • High-Level Overviews (focus on conceptual understanding):
    • Agentic AI - overview
    • Generative AI: Large Language Models (GPT, BERT), Diffusion Models, Prompt Engineering
    • Deep Learning Architectures: CNNs, RNNs, Transformers
    • Explainable AI: LIME, SHAP, Integrated Gradients (basic demos or case studies)
  • Brief Introduction to Advanced Techniques:
    • Advanced Optimization: SGD variants, distributed training (presented conceptually)
    • Edge & Federated Learning: Where privacy-preserving approaches apply

• MLOps & Functional Framework

  15%

  Junior data scientists learn the essentials of model deployment and maintenance
  Senior roles manage large-scale systems, ensure reliability, and instill best practices across teams

  Key Topics & Use Cases:

  • Conceptual MLOps Lifecycle:
    • CI/CD, Model Registries, Monitoring, Retraining
    • Highlight value of automation vs. manual steps
  • Data Pipelines & Orchestration (High-Level):
    • Airflow, Luigi, Kubeflow
  • Production Deployment Basics:
    • Containerization (Docker), Kubernetes (intro to the concepts
  • AutoML & Feature Stores (Overview):
    • How automated approaches can streamline experimentation
  • Ethical/Responsible AI (Emphasis):
    • Fairness, bias detection, and privacy considerations & frameworks