Sr Data Analyst-Hybrid/Bengaluru-Long Term Contract

About the Role

The Data Analyst is a crucial pillar in our organization, translating complex problems into actionable insights through the power of data. You will collaborate with cross-functional teams to analyze large datasets, build predictive models, and develop data-driven solutions that enhance strategic decision-making and drive organizational growth. Our ideal candidate is curious, creative, and passionate about leveraging statistical and machine learning techniques to solve real-world challenges. This role requires a self-starter with strong execution skills and the ability to work independently. You will be expected to not only execute on the current strategy but also contribute to its evolution. We are also looking for candidates who aspire to contribute to the broader data architecture community and become thought leaders in the space. We value diversity of thought and are committed to building a team that reflects the diversity of our global community.

Key Responsibilities

• Collect, process, and analyze large volumes of structured and unstructured data from diverse sources.
• Generate actionable insights to guide strategic decisions and drive improvements in technology operations/technology metrics, such as system performance, resource utilization, adherence to technology principles etc there by helping achieve improved resource utilization, boosting system reliability, reducing technical debt etc .
• Work closely with business stakeholders to understand their objectives, formulate analytical questions, and translate requirements into data-driven solutions.
• Perform exploratory data analysis (EDA) to uncover patterns, trends, and actionable insights.
• Visualize and communicate findings using compelling reports, dashboards, presentations, and data storytelling techniques tailored to technical and non-technical audiences.
• Collaborate with engineering teams to deploy predictive models into production environments and ensure their reliability, scalability, and performance.
• Monitor, evaluate, and enhance model performance over time, implementing improvements as necessary.
• Stay up to date with the latest research, tools, and best practices in data science, machine learning, and artificial intelligence.
• Contribute to a culture of experimentation, continuous learning, and innovation within the organization.

Required Qualifications

• 10+ years of being a practitioner in data engineering or a related field.
• Bachelor’s or master’s degree in computer science, Statistics, Mathematics, Data Science, Engineering, or a related quantitative field.
• Strong programming skills in languages such as Python or R; familiarity with SQL and distributed computing frameworks (e.g., Spark, Hadoop) is a plus.
• Proficiency in data visualization tools such as Matplotlib, PowerBI, Tableau.
• Solid understanding of probability, statistics, hypothesis testing, and data modeling concepts.
• Experience with cloud platforms (e.g., AWS, GCP, Azure) for data storage, processing, and model deployment is beneficial.
• Excellent communication and collaboration skills, with the ability to explain complex technical concepts to diverse audiences.
• Strong attention to detail, analytical thinking, and problem-solving abilities.

Preferred Qualifications

• Experience working on large-scale data science projects or in industry domains such as finance, healthcare, retail, or technology.
• Familiarity with MLOps practices, model versioning, and monitoring tools.
• Knowledge of natural language processing (NLP), computer vision, or time-series analysis.
• Contributions to open-source projects or publications in relevant conferences/journals.
• Develop and maintain data pipelines and ETL processes to ensure reliable and scalable data flow where applicable.
• Develop and implement machine learning algorithms and statistical models specifically aimed at analyzing technology metrics, such as system performance, resource utilization, adherence to technology principles etc there by helping achieve improved resource utilization, boosting system reliability, reducing technical debt etc .
• Hands-on experience with machine learning libraries and frameworks, such as scikit-learn, TensorFlow, Keras, PyTorch, or XGBoost.

Key Competencies

• Analytical Expertise: Comfortable working with large datasets, extracting meaningful insights, and interpreting ambiguous data patterns.
• Technical Proficiency: Deep understanding of machine learning, statistical modeling, and data engineering principles.
• Communication: Excellent written and verbal communication skills, with the ability to present insights effectively to senior leadership and non-technical stakeholders.
• Collaboration: Team-oriented mindset, capable of building strong partnerships with colleagues across various departments.
• Innovation: Commitment to creative problem-solving and willingness to experiment with novel approaches and technologies.