Senior Data Scientist

The Senior Data Scientist will play a vital role in advancing AI and machine learning capabilities aimed at Predictive and Proactive Operations. This position requires a combination of technical expertise, analytical thinking, and collaboration to develop data-driven solutions that enhance incident management workflows, ensure SLA compliance, and reduce MTTR. The Senior Data Scientist will work closely with cross-functional teams to build and optimize a robust operational framework.
Key Responsibilities:
Data Science and Model Development:
– Design and implement predictive and prescriptive models to address operational challenges.
– Develop algorithms aimed at optimizing incident workflows across multiple systems and improving SLA adherence.
– Collaborate with data engineers to build scalable data pipelines and ensure efficient data processing.
Incident Management Solutions:
– Apply advanced analytics to improve end-to-end incident workflows across various systems.
– Leverage machine learning to automate detection, triage, and escalation processes.
– Monitor and continuously refine models to ensure accuracy, efficiency, and reliability.
Collaboration and Stakeholder Engagement:
– Work closely with product teams, service delivery managers, and architects to align data science solutions with business objectives.
– Serve as a subject matter expert, providing insights and recommendations to enhance operational processes.
– Translate business requirements into actionable data science solutions.
Continuous Improvement:
– Analyze existing processes to identify inefficiencies and propose data-driven improvements.
– Stay current with industry trends, tools, and technologies to integrate best practices into team workflows.
– Contribute to building a library of reusable models and solutions to support the broader organization.
Data Visualization and Reporting:
– Develop dashboards and reports to visualize insights, track SLA compliance, and measure improvements in MTTR.
– Effectively communicate findings and progress to both technical and non-technical stakeholders.
Required Qualifications:
– Bachelor’s or Master’s degree in Data Science, Computer Science, Statistics, Mathematics, or a related field.
– Certifications in AI, machine learning, or data engineering are a plus.
Experience:
– 5+ years of experience in data science, focusing on machine learning and predictive analytics.
– Proven track record of deploying machine learning models in production environments.
– Previous experience in incident management or operational optimization is highly desirable.
Technical Proficiency:
– Strong programming skills in Python, R, and SQL.
– Proficiency in machine learning frameworks like Scikit-learn, TensorFlow, or PyTorch.
– Familiarity with cloud platforms (e.g., Azure, GCP, AWS) and MLOps tools.
– Experience with ITSM platforms (e.g., ServiceNow) and monitoring tools (e.g., Splunk, Dynatrace).
Skills:
– Strong analytical and problem-solving capabilities.
– Ability to work effectively in cross-functional teams.
– Excellent communication skills, with the ability to explain complex concepts to non-technical stakeholders.
Preferred Qualifications:
– Hands-on experience with Predictive and Proactive Operations or similar domains.
– Knowledge of incident, event, or workflow management processes.
– Familiarity with SLA-driven operational environments.
Key Behaviors:
– Data-driven mindset focused on customer-centric solutions.
– Proactive and adaptable, with a passion for continuous learning.
– Strong collaborative skills, with the ability to work across different teams and departments.
Specific Needs in Predictive and Proactive Operations:
– Ability to design and implement models that enhance multi-system operational efficiency.
– Focus on reducing IT operations incidents, false positive alerts, MTTR, and improving SLA compliance.
– Contribution to the development of scalable and reusable data science solutions.