Data Scientist

Job Description :

We are seeking a highly skilled and motivated Data Scientist with hands-on experience in designing, developing, and deploying anomaly detection algorithms. The ideal candidate will have a strong background in machine learning, statistical modeling, and time-series analysis, with a passion for solving complex data problems and uncovering hidden patterns.

Key Responsibilities:

• Design and implement robust anomaly detection models for structured and unstructured data.
• Analyze large datasets to identify trends, patterns, and outliers.
• Develop scalable solutions for real-time and batch anomaly detection.
• Collaborate with data engineers, product managers, and business stakeholders to define problem statements and deliver actionable insights.
• Evaluate and compare different algorithms (e.g., Isolation Forest, Autoencoders, One-Class SVM, etc.) for anomaly detection.
• Monitor model performance and continuously improve accuracy and efficiency.
• Document methodologies, experiments, and results for internal and external stakeholders.

Required Qualifications:

• Bachelor's or master's degree in computer science, Statistics, Mathematics, or a related field.
• 3+ years of experience in data science or machine learning roles.
• Proven experience in building and deploying anomaly detection systems.
• Proficiency in Python (NumPy, pandas, scikit-learn, TensorFlow/PyTorch).
• Strong understanding of statistical methods, time-series analysis, and unsupervised learning techniques.
• Experience with data visualization tools (e.g., Matplotlib, Seaborn, Plotly).
• Familiarity with cloud platforms (AWS, Google Cloud Platform, or Azure) and big data tools (Spark, Hadoop) is a plus.

Preferred Skills:

• Experience with streaming data and real-time anomaly detection (e.g., Kafka, Flink).
• Knowledge of domain-specific anomaly detection use cases (e.g., fraud detection, system monitoring, IoT).
• Strong communication skills and ability to explain complex models to non-technical stakeholders.