Senior Data Scientist

Company: Alfahive

Website: Visit Website

Business Type: Startup

Company Type: Product & Service

Business Model: B2B

Funding Stage: Bootstrapped

Industry: Cybersecurity

Salary Range: ₹ 15-35 Lacs PA

Job Description

About Alfahive:

Alfahive is a global technology and cybersecurity company specializing in cyber risk management and AI-driven solutions. Founded in 2021, the company helps organizations assess and mitigate risks through intelligent automation and data-driven insights. With operations in the US and India, Alfahive supports enterprises in strengthening security and enabling digital transformation.

Role Description

We are seeking a highly skilled Data Scientist with strong MLOps expertise to bridge the gap between data science model development and production deployment. This role requires a strong foundation in data science, with expertise in model development, optimization, and experimentation, as well as the ability to operationalize these models through robust MLOps practices. You will work closely with data engineers, software engineers, and DevOps teams to optimize machine learning workflows, ensuring reliability, performance, and maintainability.

Key Responsibilities

• Model Development & Optimization: Design, train, evaluate, and fine-tune machine learning models to solve business problems.
• Experimentation & Feature Engineering: Develop and test features, optimize hyperparameters, and enhance model performance using scalable solutions.
• Data Engineering Collaboration: Work alongside data engineers to streamline data pipelines, ensuring high-quality data for training and inference.
• End-to-End ML Lifecycle Management: Deploy, monitor, and continuously improve ML models in production environments.
• MLOps Pipeline Development: Automate CI/CD pipelines for ML workflows, enabling efficient model training, testing, and deployment.
• Model Monitoring & Governance: Implement model performance tracking, version control, and drift detection mechanisms.
• Infrastructure & Scalability: Optimize cloud and on-prem infrastructure for ML workloads, leveraging containerization (Docker, Kubernetes) and orchestration tools.
• Security & Compliance: Ensure ML systems meet enterprise security, privacy, and regulatory compliance standards.
• Cross-Team Collaboration: Act as a bridge between data science, software engineering, and DevOps teams to operationalize AI solutions effectively.
  
  

• ML Model Development: Strong experience with ML/DL frameworks (PyTorch, TensorFlow, Scikit-learn, XGBoost, etc.).
• Experimentation & Feature Engineering: Expertise in model tuning, feature selection, and hyperparameter optimization.
• Data Engineering: Proficiency in Databricks Spark, Airflow, and feature stores for ML data pipelines.
• Programming & Scripting: Strong Python skills and Fast API Skills; familiarity with Bash, SQL, Pandas and infrastructure-as-code tools.
• MLOps & Automation: Proficiency in tools like Azure Devops and Shell Scripting
• Cloud & Infrastructure: Experience with cloud platforms (Azure/OnPrem) and infrastructure automation.
• CI/CD & DevOps: Expertise in CI/CD tools (GitHub Actions, Azure DevOps) for ML pipelines.
• Containerization & Orchestration: Hands-on experience with Docker, Kubernetes, and Helm for scalable ML deployment.
• Monitoring & Logging: Experience with Prometheus, Grafana, and ELK stack for model monitoring.
• Version Control & Reproducibility: Experience with Git and model versioning practices.
  
  

• 3–8 yrs of experience in Data Scientist / MLOps.
• Experience with feature engineering and automated feature selection techniques.
• Familiarity with edge AI deployment strategies.
• Experience with real-time inference and streaming data processing.
• Strong skills on python, pandas and streamlit
  
  

• Understanding of AI governance, fairness, and explainability frameworks.
  
  

• Worked on Natural Language Processing (NLP) projects, including text classification, entity recognition, and sentiment analysis.
• Developed and deployed predictive models for business insights, fraud detection, or customer behavior analysis.
• Has used/Implemented Transformer-based models (BERT, GPT, T5, etc.) for various AI applications. Has used APIs via OLlama or other engines.
• Built recommendation systems to personalize user experiences and optimize decision-making.
• Designed and maintained real-time ML inference pipelines for high-traffic applications.