Principal Solutions Architect

2 months ago

Pleasanton, United States CriticalRiver Inc. Full time

Project/Task:

A key player in shaping our data science strategies and driving innovative solutions. This role requires a seasoned professional with a deep understanding of data science, exceptional leadership skills, and the ability to architect and deliver cutting-edge data-driven solutions. Join us to lead the charge in transforming data into actionable insights and driving business success through strategic technical leadership.

Strategic Leadership:

Collaborate with executive leadership to define and execute the data science strategy aligned with business objectives.

Provide thought leadership in data science and analytics, influencing decision-making at the highest levels.Drive innovation by identifying and implementing emerging technologies and methodologies.

Solution Architecture:

Architect end-to-end data science solutions, ensuring scalability, reliability, and adherence to best practices.

Collaborate with cross-functional teams to define technical requirements and design robust data architectures.

Lead the development and implementation of advanced analytics and machine learning models.

Team Management:

Lead and mentor a team of data scientists and solution architects.

Foster a collaborative and results-driven team culture, encouraging continuous learning and development.

Provide guidance on complex technical challenges and drive excellence in project execution.

Client Collaboration:

Collaborate with clients and internal stakeholders to understand business challenges and develop tailored data science solutions.

Act as a trusted advisor, translating business needs into technical requirements and delivering high-impact solutions.

Build and maintain strong client relationships, ensuring satisfaction and success.

Innovation and Research:

Stay abreast of industry trends, emerging technologies, and best practices in data science.

Lead research initiatives to explore and integrate new tools and methodologies.

Drive a culture of innovation and continuous improvement within the data science team.

Quality Assurance:

Establish and enforce data quality and governance standards.

Conduct regular reviews and audits to ensure the integrity and accuracy of data-driven solutions.

Collaborate with relevant stakeholders to address data-related challenges.


Qualifications:

Advanced degree (Ph.D. or Master's) in Computer Science, Data Science, or a related field.

Proven experience (minimum of 10 years) in data science, with a focus on solution architecture and leadership.

Expertise in machine learning, statistical modeling, data engineering, data modeling and data visualization.

Strong programming skills in languages such as Python, R, or Scala.

Experience with big data technologies and cloud platforms (e.g., AWS, Azure, GCP).

Experience with AI/ML and cloud platforms (e.g., Data Bricks, Domino Data, Palantir AWS, Azure, GCP).

Excellent leadership, communication, and stakeholder management skills.

Demonstrated success in leading and delivering complex data science projects.

Strong problem-solving and critical-thinking abilities.

Data Science & Machine Learning:

Proficiency in machine learning algorithms and techniques (supervised, unsupervised, reinforcement learning).

Experience with data preprocessing, feature engineering, and model evaluation.

Familiarity with deep learning frameworks (e.g., TensorFlow, PyTorch).

Programming & Software Development:

Strong coding skills in languages such as Python, R, Java, or Scala.

Experience with software development practices, including version control (Git), testing, and continuous integration/continuous deployment (CI/CD).

Big Data Technologies:

Proficiency in big data frameworks and tools (e.g., Hadoop, Spark, Kafka).

Experience with data warehousing solutions (e.g., Snowflake, Redshift) and distributed computing.

Data Management & Storage:

Knowledge of SQL and NoSQL databases (e.g., MySQL, PostgreSQL, MongoDB, Cassandra).

Experience with data lakes and data warehouses.

Cloud Platforms:

Proficiency in cloud services (e.g., AWS, Azure, Google Cloud) for deploying and managing data science solutions.

Experience with cloud-based machine learning services (e.g., AWS SageMaker, Azure Machine Learning).

Architectural Skills

System Architecture Design:

Ability to design scalable, reliable, and secure architectures for data science solutions.

Understanding of microservices architecture, RESTful APIs, and service-oriented architecture (SOA).

Integration & Interoperability:

Experience in integrating various data sources and systems.

Knowledge of API design and integration patterns.

Data Pipeline & Workflow Management:

Proficiency in designing and managing ETL/ELT processes.

Experience with workflow orchestration tools (e.g., Apache Airflow, Luigi).

Analytical & Problem-Solving Skills

Analytical Thinking:

Strong problem-solving skills and the ability to analyze complex datasets to derive actionable insights.

Proficiency in statistical analysis and hypothesis testing.

Business Acumen:

Ability to understand business problems and translate them into data science solutions.

Experience in collaborating with stakeholders to gather requirements and deliver solutions that meet business needs.

Communication & Collaboration Skills

Effective Communication:

Strong verbal and written communication skills to explain complex technical concepts to non-technical stakeholders.

Experience in creating technical documentation and presenting findings.

Collaboration & Leadership:

Ability to lead cross-functional teams and collaborate with data scientists, engineers, and business analysts.

Experience in mentoring and guiding junior team members.

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