Senior Data Scientist

Job Description Senior Data Scientist (P3) 

Location : Bangalore 

Experience : 5 - 8 years 

 

About New Relic

New Relic is a cloud-based observability platform that gives developers, engineers, operations, and management a clear view of what’s happening in today’s complex software environments. So they can find and fix problems faster, and deliver delightful experiences for their customers. That's why the world’s best engineering teams rely on New Relic to visualize, analyze, and troubleshoot their software. It’s the simplest, most powerful cloud-based observability platform, built to create more perfect software. All from one place.

Founded in 2008, we’re a global company passionate about building a culture where all employees feel a deep sense of belonging, where every ‘Relic’ can bring their whole self to work and feel supported and empowered to thrive. We’re consistently recognized as a distinguished employer and are committed to building world-class products and an award-winning culture. For more information, visit newrelic.com.

Job Description: Senior Data Scientist

Why This Role Matters: Most GenAI roles right now are drowning in hype, focusing purely on prompt engineering with little mathematical substance. On the flip side, many traditional data science roles are stuck in silos, far away from production. This role is the antidote to both. At New Relic, you will sit at the intersection of massive-scale telemetry data and cutting-edge intelligence. You will use your deep statistical foundations to solve complex, real-world time-series and anomaly detection problems, while simultaneously having the mandate to build the next generation of AI Agents and LLM orchestration for autonomous observability. You won't just build models in a vacuum—you will deploy them, own them, and see their impact on millions of applications globally.
Description

We are seeking an experienced and dynamic Senior Data Scientist to join our team at New Relic. You will be primarily responsible for driving data-oriented projects, transforming ambiguous business problems into clear, actionable insights, and productionalizing those insights at scale. The ideal candidate is adept at understanding business needs that are often quantitatively ambiguous and using large, complex, structured datasets (such as telemetry, time-series, and system logs) to find opportunities for product and process optimization. You should be able to work independently, take an idea and run with it, and deliver high-impact results while bridging the gap between classical ML modeling and the next wave of agentic AI.

Responsibilities

• Predictive Analytics: Analyze large, complex structured datasets, applying robust statistical methods and classical machine learning techniques (e.g., time-series forecasting, anomaly detection, classification, and linear/logistic regression).
• Model Deployment: End-to-end ownership of data science solutions, including building, evaluating, and deploying models into production with clean version control and CI/CD practices.
• Rigorous Evaluation: Establish and manage model evaluation frameworks to track model accuracy, data drift, and performance metrics post-deployment.
• GenAI Exploration: Collaborate on innovative initiatives by building AI Agents and exploring LLM orchestration to advance autonomous observability capabilities.
• Cross-Functional Collaboration: Partner effectively with internal stakeholders, engineering teams, and product managers to understand technical needs and translate data findings into actionable product plans.
• Communication & Visualization: Present complex analytical findings using data visualization techniques, clearly communicating recommendations to both technical and non-technical stakeholders.

Required Qualifications

• Experience: 5+ years of professional experience as a Data Scientist or in a highly quantitative technical role.
• Education: Bachelor’s degree or more.
• Statistical Mastery: Strong foundational understanding and practical application of advanced statistical concepts, classical machine learning algorithms ,Deep Learning and time-series analysis.
• Technical Proficiency: Expert-level skills in Python and SQL for data manipulation, modeling, and pipeline development.
• Production Experience: Proven track record of taking data science models out of notebooks and successfully deploying them into a live production environment.
• Soft Skills: Exceptional problem-solving abilities, should be comfortable navigating ambiguity, good communication skills, and the ability to manage multiple competing priorities independently with minimal supervision.
• Stakeholder Management: Prior experience translating complex data science methodologies into clear, high-impact narratives for leadership and non-technical audiences.

Good to Have

• Domain Expertise: Experience in the Observability, APM (Application Performance Monitoring), or AIOps space, particularly working with large-scale telemetry data (metrics, events, logs, and traces).
• GenAI Exposure: Hands-on experience or deep familiarity with LLM orchestration frameworks (e.g., LangChain, LlamaIndex, CrewAI) and building practical AI Agents.
• Modern MLOps: Exposure to advanced deployment infrastructure like Docker, Kubernetes, and scaling PODs in cloud environments.

Key Pillars to Highlight to Candidates

We can emphasize these three things:

• Real Data at Massive Scale: You aren't working on boring, clean tabular data. You are tackling high-velocity, complex telemetry data where your statistical rigor actually matters.
• The Best of Both Worlds: You don't have to choose between keeping your classical ML skills sharp and learning GenAI. We want you to use your strong math background and give you the playground to build AI agents.
• True End-to-End Ownership: You won't just hand code over to an engineering team to die in a queue. You’ll have the autonomy to build, evaluate, and push your models to production.