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Data Scientist (Algorithm & Machine Learning Focused)

Job Description

Exp - 7+ Years

We are seeking a highly skilled Data Scientist with a strong foundation in machine learning, analytics, and algorithmic problem-solving. The ideal candidate will have expertise in data structures and algorithms (competitive coding level) along with hands-on experience in building and deploying scalable data science solutions. This role requires working across the full lifecycle—from model development to production implementation—within the finance and market domain.

Key Responsibilities

Develop and implement mathematical models and algorithms to solve complex business problems.

Design, train, and optimize machine learning models for predictive analytics and decision-making.

Apply strong data structures and algorithmic concepts to build efficient and scalable solutions.

Analyze large datasets to extract meaningful insights and support business strategies.

Work on financial and market data for use cases such as forecasting, risk analysis, and pattern detection.

Collaborate with cross-functional teams to design, develop, and deploy data-driven solutions.

Manage end-to-end model lifecycle including data processing, model building, validation, deployment, and monitoring.

Implement MLOps practices to ensure smooth deployment and scalability of models.

Create visualizations and reports to communicate insights effectively to stakeholders.

Ensure data quality, integrity, and model performance optimization.

Required Skills & Qualifications

Strong proficiency in Data Structures & Algorithms (competitive coding level preferred).

Hands-on experience with Python and SQL.

Solid understanding of machine learning techniques, statistics, and model evaluation.

Experience handling large datasets and performing data analysis.

Exposure to finance/market domain is preferred.

Experience in model deployment, MLOps, and production environments.

Strong analytical thinking and problem-solving skills.

Preferred Skills

Experience with ML libraries such as Scikit-learn, TensorFlow, or PyTorch.

Familiarity with cloud platforms (AWS, Azure, or GCP).

Experience with real-time systems or high-performance data processing.