Data Scientist Architect

Responsibilities:

Strategic Data Planning and Architecture:

• Lead the development of our data architecture strategy, encompassing data collection, storage, processing, and analysis.
• Design scalable and efficient data pipelines and architectures to support our growing data needs.
• Collaborate with cross-functional teams to ensure alignment with business goals and objectives.

Advanced Analytics and Modeling:

• Develop and deploy sophisticated machine learning models and algorithms to extract actionable insights from complex datasets.
• Apply statistical techniques and advanced analytics to solve business problems and drive decision-making.
• Innovate in areas such as deep learning, natural language processing, and predictive modeling to stay at the forefront of data science.

Data Governance and Compliance:

• Establish and enforce data governance policies and best practices to ensure data quality, integrity, and security.
• Ensure compliance with regulatory requirements such as GDPR, HIPAA, and CCPA.
• Implement data privacy and anonymization techniques to protect sensitive information.

Model Deployment and Integration:

• Architect robust model deployment pipelines for seamless integration into production environments.
• Collaborate with IT and DevOps teams to deploy and monitor models at scale.
• Optimize model performance and scalability for real-time inference and decision-making.

Leadership and Mentorship:

• Provide technical leadership and guidance to junior data scientists and analysts.
• Foster a culture of innovation, collaboration, and continuous learning within the data science team.
• Act as a subject matter expert on data science and architecture, both internally and externally.

Required Skills/Experience:

• Proficiency in programming languages such as Python, R, or Scala, with a focus on data manipulation, analysis, and modeling.
• Expertise in data visualization tools and libraries such as Matplotlib, Seaborn, Plotly, or D3.js.
• Deep understanding of database technologies including SQL and NoSQL databases (e.g., PostgreSQL, MongoDB, Cassandra).
• Experience with big data technologies such as Hadoop, Spark, or Kafka for processing and analyzing large datasets.
• Knowledge of distributed computing frameworks such as Apache Hadoop, Apache Spark, or Apache Flink.
• Familiarity with containerization technologies such as Docker and container orchestration tools like Kubernetes.
• Experience with version control systems such as Git for code management and collaboration.
• Strong understanding of software engineering principles, including modular design, testing, and debugging.
• Knowledge of agile methodologies and experience working in agile development environments.
• Familiarity with data engineering concepts and tools for data ingestion, transformation, and cleansing.
• Experience with streaming data technologies and real-time data processing frameworks (e.g., Apache Kafka, Apache Flink, Apache Storm).
• Understanding of machine learning operations (MLOps) principles and practices for managing machine learning lifecycle.
• Familiarity with natural language processing (NLP) techniques and libraries such as NLTK, spaCy, or TensorFlow.

Qualification:

• Bachelor’s or Master’s degree in Computer Science, Statistics, Mathematics, or related field.
• 12-14 years of experience in data science, with a focus on architecture and design.
• Proven expertise in designing and implementing data architectures and solutions at scale.
• Strong proficiency in machine learning, statistical analysis, and data mining techniques.
• Experience with cloud platforms such as AWS, Azure, or Google Cloud.
• Excellent communication and interpersonal skills, with the ability to communicate complex technical concepts to non-technical stakeholders.
• Leadership experience, with a track record of leading cross-functional teams and driving strategic initiatives.