Urgent Openings For ML_ Data Scientist – Anomaly Detection

Excelacom Technologies is looking for an experienced Machine Learning Data Scientist with strong expertise in Anomaly Detection to lead the design, development, and deployment of advanced machine learning solutions. The ideal candidate will have hands-on experience in building production-grade ML models, managing teams, and working directly with clients to solve complex business problems.

Candidates with proven experience in Anomaly Detection are highly preferred.

Company: Excelacom Technologies Pvt. Ltd.

Location: Siruseri, Chennai

Work Mode: Work from Office

Experience: 5 20 Years

Notice Period: Immediate Joiners / Maximum 15 Days Preferred
Key Responsibilities

Data Exploration & Engineering

• Collect, clean, preprocess, and transform structured and unstructured datasets.

• Perform exploratory data analysis (EDA) to identify patterns, trends, and anomalies.

• Engineer features for supervised, unsupervised, and time-series models.

• Work with enterprise datasets including logs, transactional data, sensor/IoT data, and CRM data.

• Implement data validation, quality checks, and data augmentation pipelines.

Model Development & Evaluation

• Design and develop machine learning models for classification, regression, clustering, and anomaly detection.

• Apply anomaly detection techniques such as:

• Isolation Forest

• Autoencoders

• One-Class SVM

• Time-Series Anomaly Detection

• Evaluate models using metrics such as Precision, Recall, F1 Score, AUC-ROC, RMSE, etc.

• Manage model drift, retraining strategies, and continuous model improvement.

• Work on RAG pipelines and vector search optimization for GenAI-powered analytics.

Machine Learning & Deep Learning Engineering

• Build ML/DL pipelines using Python, PyTorch, TensorFlow, and Scikit-learn.

• Develop NLP and time-series forecasting solutions.

• Optimize models for performance, latency, scalability, and cost efficiency.

• Integrate ML solutions into AI automation frameworks such as LangChain and CrewAI.

Team Leadership & Management

• Lead, mentor, and manage a team of Data Scientists and ML Engineers.

• Drive best practices in coding standards, experimentation, documentation, and reproducibility.

• Plan and manage project timelines, sprint activities, and deliverables.

• Support team capability development in ML, DL, and MLOps.

Client Engagement

• Act as the primary point of contact for client stakeholders.

• Translate business requirements into ML problem statements and measurable success criteria.

• Present insights, model outcomes, and recommendations to business audiences.

• Define and prioritize use cases such as:

• Anomaly Detection

• Forecasting

• Churn Prediction

• Predictive Analytics

Monitoring & Production Support

• Monitor deployed models for accuracy, latency, and drift.

• Establish dashboards, alerts, and monitoring for anomaly detection systems.

• Collaborate with engineering teams on CI/CD and MLOps implementation.

Security & Governance

• Ensure secure handling of enterprise and sensitive data.

• Maintain compliance with governance, audit, and regulatory requirements.

• Implement safeguards against data leakage and promote responsible AI practices.

Required Skills & Expertise
Core Data Science & Machine Learning

• Strong knowledge of Statistics, Machine Learning, and Deep Learning.

• Hands-on experience in Anomaly Detection and Time-Series Analysis.

• Strong understanding of model performance evaluation methodologies.

Technical Skills

• Python, SQL

• Scikit-learn, PyTorch, TensorFlow

• Power BI, Tableau, Matplotlib, Seaborn

• AWS, Azure, or GCP

• MLOps tools and deployment frameworks

Leadership & Communication

• Experience leading or mentoring Data Science / ML teams.

• Strong client-facing communication and stakeholder management skills.

• Ability to convert business requirements into technical solutions.

Architecture & Deployment

• Experience with API-based model serving and microservices architecture.

• Understanding of cloud-native and on-premise deployment environments