Senior Software Engineer

Designation / Title

Data Scientist II

Experience (Years)

5+ Yrs

Joining Location

Pune

About Quick Heal

Quick Heal Technologies Ltd, founded in 1995 and headquartered in Pune, is India’s leading listed cybersecurity solutions provider with 23 offices and 1,000+ professionals. Through its enterprise arm Seqrite, the company delivers cutting-edge Zero Trust solutions to secure endpoints, data, networks, and users across industries and geographies.

Seqrite Labs powers its offerings with advanced threat research, intelligence, and real-time detection. Notably, Seqrite secured ISRO’s command & control center during the Chandrayaan 3 mission. Its services division provides consulting to corporates, PSUs, government, and law enforcement agencies worldwide.

Core Purpose: Innovate to simplify digital security
Vision: To be trusted globally as a cybersecurity leader
Mission: Empower teams to solve business problems

Seqrite stands out as a purpose-led organization that invests in people, fosters innovation, and offers opportunities to work on new technologies while building a safer digital world.

Job Summary

(A brief overview of the role, its purpose, and key responsibilities.)

The Data Scientist will be responsible for analyzing data, building machine learning models, and generating insights to support cybersecurity and product use cases. The role involves data preprocessing, feature engineering, model development, model evaluation, and working with engineering teams to integrate ML outputs into applications, dashboards, or workflows.

The candidate should have hands-on experience in Python, SQL, machine learning techniques, data analysis, and basic MLOps concepts. Experience in cybersecurity analytics, anomaly detection, user behavior analytics, or AI/ML-based product development will be an added advantage.

Job Responsibilities

(The key tasks and duties you will be expected to perform in this role.)

·         Understand business and product requirements and convert them into data analysis or machine learning tasks.

·         Collect, clean, preprocess, and analyze structured and unstructured datasets from different sources.

·         Perform exploratory data analysis to identify trends, patterns, anomalies, and useful insights.

·         Build and validate machine learning models for classification, prediction, clustering, anomaly detection, and risk scoring use cases.

·         Perform feature engineering to improve model accuracy and business relevance.

·         Evaluate model performance using suitable metrics such as accuracy, precision, recall, F1-score, and false positive rate.

·         Work with engineering teams to integrate ML models or analytical outputs into products, APIs, reports, or dashboards.

·         Support model deployment, monitoring, retraining, and performance tracking using basic MLOps practices.

·         Prepare reports, visualizations, and documentation to communicate findings and model outcomes.

·         Collaborate with product, engineering, security, and data teams to deliver practical data-driven solutions.

·         Maintain proper documentation for datasets, model logic, assumptions, evaluation results, and limitations.

·         Continuously learn and apply relevant AI/ML techniques, tools, and frameworks based on project needs.

·         Strong communication, troubleshooting, co-ordination and interpersonal skill

Must Have Skills
(The essential skills and expertise required to succeed in this role.)

·         Bachelor’s/Master’s degree in Computer Science, Data Science, Statistics, Mathematics, Engineering, or equivalent.

·         3–6 years of hands-on experience in Data Science, Machine Learning, or AI/ML solution development.

·         Strong programming skills in Python.

·         Good knowledge of SQL and experience working with structured/unstructured datasets.

·         Hands-on experience with data preprocessing, feature engineering, exploratory data analysis, and model evaluation.

·         Good understanding of ML algorithms such as classification, regression, clustering, anomaly detection, and predictive modeling.

·         Experience with Python libraries such as Pandas, NumPy, Scikit-learn, and visualization libraries.

·         Basic understanding of MLOps, including model deployment, monitoring, retraining, and versioning.

·         Ability to analyze data, identify patterns, and present insights clearly.

·         Good communication, documentation, and cross-functional collaboration skills.

Nice / Good to have skills

(Additional skills that are not mandatory but give you an added advantage in this role.)

·         Exposure to cybersecurity

·         Exposure to Generative AI, LLMs, RAG, prompt engineering, or Agentic AI concepts.

·         Experience with dashboards or visualization tools such as Power BI, Kibana, or Grafana