Technical Lead

HCL Technologies Limited; Ajax; On-site; CAD 90,000 - 140,000; Full time

Job summary

HCLTech is seeking a Safety Operations & Automation Data Scientist to design, implement, and maintain intelligent automation across our safety filter workflows. You will replace manual review bottlenecks in Buganizer with ML-based classifications, routing and parsing of allowlist requests, and build real-time visibility dashboards.

You will collaborate with data engineering, operations, and product to develop data pipelines, enable event-driven triggers via Buganizer API, and drive proactive

Qualifications

• Experience designing ML-based automation to classify, route and parse safety filter requests.
• Ability to build robust data pipelines and ensure accurate SLA tracking.
• Familiarity with Buganizer or similar ticketing systems and API automation.

Responsibilities

• Buganizer Workflow Automation: Design, build, and deploy ML-based models to automatically classify, route, and parse incoming safety filter allowlist requests within Buganizer.
• Automated Reporting & Analytics: Create real-time dashboards connected to Buganizer data to monitor operational health metrics (volume, processing time, SLA breach risks).
• Automated Stakeholder Communications: Implement event-driven triggers via the Buganizer API to proactively update Account Teams and customers on ticket statuses.

Skills

ML models

Data pipelines

Automation

Buganizer API

Stakeholder communications

Tools

Buganizer API

Dashboard tools

Job description

Safety Filter Data Scientist RequestExecutive SummaryAs our customer base and usage volumes scale, the operational overhead of managing Safety Filter reviews has become a significant bottleneck. Currently, the end-to-end process—from initial review in Buganizer to stakeholder communication—relies heavily on manual intervention. We are requesting a temp headcount for a Safety Filter Data Scientist to design, implement, and maintain intelligent automation across our safety filter workflows. This role will reduce manual toil, improve SLA adherence, and enhance the experience for both our customers and account teams.The Problem: Manual Bottlenecks in BuganizerOur current safety filter process is highly manual and presents several scaling challenges:Manual Buganizer Triage: Human reviewers are spending excessive time manually reading through lengthy form submissions, prompt examples, and risk mitigation plans within Buganizer tickets to determine if an allowlist request is justified.The Stakeholder Black Hole & "Ping-Pong" Comments: Because updates aren't automated, Sales PoCs and Account Managers are left in the dark regarding ETAs. This results in stakeholders constantly tagging reviewers in Buganizer comments (e.g., "Do you have any update?"), which distracts the team and slows down the actual review process.Fragmented Reporting: Extracting actionable insights, tracking SLOs/SLAs across different priority tiers, and volume forecasting requires manual data wrangling from Buganizer, leading to delayed decision-making.Develop ML models and heuristic-based logic to auto-parse incoming Buganizer tickets, automatically categorizing or flagging requests based on historical data, project IDs, and the provided prompt context.Reduce the human-in-the-loop requirement to only edge cases and high-risk escalations, dramatically speeding up average resolution times.Design and deploy real-time dashboards connected directly to Buganizer data to track operational health metrics (e.g., request volume, processing time, SLA breach risks).Engineer event-driven triggers within Buganizer to automatically update Account Teams and customers on request statuses or when additional information is required.Ensure stakeholders are proactively notified of state changes (e.g., "In Review," "Approved"), eliminating the need for manual status pings in the comment threads.The Solution: Responsibilities of the Data ScientistThis role will bridge the gap between operations and technical infrastructure by focusing on three key pillars of automation:Buganizer Workflow & Triage AutomationAutomated Reporting & AnalyticsAutomated Stakeholder Communication WorkflowsJob Description: Data Scientist, Safety Operations & AutomationAbout the Role:As our platform scales, ensuring a safe and compliant environment for our users is our top priority. The Safety Operations team is seeking a Data Scientist to spearhead the automation of our safety filter review processes. In this role, you will act as the bridge between data engineering, operations, and product. You will be responsible for replacing manual review bottlenecks in our ticketing systems with intelligent, automated workflows, building real-time visibility dashboards, and streamlining stakeholder communications. Your work will directly reduce time-to-resolution, improve SLA adherence, and scale our Trust & Safety operations efficiently.Key Responsibilities:Buganizer Workflow Automation: Design, build, and deploy ML-based models to automatically classify, route, and parse incoming safety filter allowlist requests directly within Buganizer.Data Pipeline & Infrastructure: Build and maintain robust data pipelines that connect our safety backend and Buganizer to ensure seamless data flow and accurate SLA tracking.Automated Stakeholder Communications: Engineer event-driven triggers via the Buganizer API to automatically update Account Teams and customers on ticket statuses, eliminating manual comment updates and email threads.R

Key Responsibilities

Safety Filter Data Scientist RequestExecutive SummaryAs our customer base and usage volumes scale, the operational overhead of managing Safety Filter reviews has become a significant bottleneck. Currently, the end-to-end process—from initial review in Buganizer to stakeholder communication—relies heavily on manual intervention. We are requesting a temp headcount for a Safety Filter Data Scientist to design, implement, and maintain intelligent automation across our safety filter workflows. This role will reduce manual toil, improve SLA adherence, and enhance the experience for both our customers and account teams.The Problem: Manual Bottlenecks in BuganizerOur current safety filter process is highly manual and presents several scaling challenges:Manual Buganizer Triage: Human reviewers are spending excessive time manually reading through lengthy form submissions, prompt examples, and risk mitigation plans within Buganizer tickets to determine if an allowlist request is justified.The Stakeholder Black Hole & "Ping-Pong" Comments: Because updates aren't automated, Sales PoCs and Account Managers are left in the dark regarding ETAs. This results in stakeholders constantly tagging reviewers in Buganizer comments (e.g., "Do you have any update?"), which distracts the team and slows down the actual review process.Fragmented Reporting: Extracting actionable insights, tracking SLOs/SLAs across different priority tiers, and volume forecasting requires manual data wrangling from Buganizer, leading to delayed decision-making.Develop ML models and heuristic-based logic to auto-parse incoming Buganizer tickets, automatically categorizing or flagging requests based on historical data, project IDs, and the provided prompt context.Reduce the human-in-the-loop requirement to only edge cases and high-risk escalations, dramatically speeding up average resolution times.Design and deploy real-time dashboards connected directly to Buganizer data to track operational health metrics (e.g., request volume, processing time, SLA breach risks).Engineer event-driven triggers within Buganizer to automatically update Account Teams and customers on request statuses or when additional information is required.Ensure stakeholders are proactively notified of state changes (e.g., "In Review," "Approved"), eliminating the need for manual status pings in the comment threads.The Solution: Responsibilities of the Data ScientistThis role will bridge the gap between operations and technical infrastructure by focusing on three key pillars of automation:Buganizer Workflow & Triage AutomationAutomated Reporting & AnalyticsAutomated Stakeholder Communication WorkflowsJob Description: Data Scientist, Safety Operations & AutomationAbout the Role:As our platform scales, ensuring a safe and compliant environment for our users is our top priority. The Safety Operations team is seeking a Data Scientist to spearhead the automation of our safety filter review processes. In this role, you will act as the bridge between data engineering, operations, and product. You will be responsible for replacing manual review bottlenecks in our ticketing systems with intelligent, automated workflows, building real-time visibility dashboards, and streamlining stakeholder communications. Your work will directly reduce time-to-resolution, improve SLA adherence, and scale our Trust & Safety operations efficiently.Key Responsibilities:Buganizer Workflow Automation: Design, build, and deploy ML-based models to automatically classify, route, and parse incoming safety filter allowlist requests directly within Buganizer.Data Pipeline & Infrastructure: Build and maintain robust data pipelines that connect our safety backend and Buganizer to ensure seamless data flow and accurate SLA tracking.Automated Stakeholder Communications: Engineer event-driven triggers via the Buganizer API to automatically update Account Teams and customers on ticket statuses, eliminating manual comment updates and email threads.R

At HCLTech, you'll supercharge your potential. You'll find your career. And you'll find your spark. All at a place that knows that helping its customers stay on top starts by putting its people first.

HCLTech is a global technology company, home to more than 223,000 people across 60 countries, delivering industry-leading capabilities centered around digital, engineering, cloud and AI, powered by a broad portfolio of technology services and products. We work with clients across all major verticals, providing industry solutions for Financial Services, Manufacturing, Life Sciences and Healthcare, Technology and Services, Telecom and Media, Retail and CPG, and Public Services. Consolidated revenues as of 12 months ending June 2026totaled $14.8billion.