Techversant Infotech - Senior Data Scientist - Predictive Modeling

Description

• The ideal candidate combines deep expertise in data engineering, machine learning, and statistical modeling with strong leadership skills to mentor teams, influence business strategy, and build scalable AI/ML-driven systems.
  

Key Responsibilities

• Lead the end-to-end lifecycle of data science projects from data exploration and model development to deployment and monitoring.
• Architect and implement predictive modeling, machine learning, and AI solutions to address complex business challenges.
• Guide data preprocessing, feature engineering, and validation for structured and unstructured datasets.
• Collaborate with cross-functional teams (Business, Product, and Engineering) to translate business objectives into data-driven strategies.
• Design and enhance data pipelines and collection frameworks to improve data availability and quality.
• Conduct exploratory data analysis (EDA) to identify trends, patterns, and opportunities for innovation.
• Evaluate and select appropriate ML algorithms, optimize models for accuracy and scalability, and ensure production readiness.
• Present analytical insights and recommendations to senior leadership in a clear and actionable format.
• Mentor junior data scientists and establish best practices in coding, modeling, and experimentation.
• Stay current with emerging tools, technologies, and methodologies in data science and AI.
  

Required Skills & Expertise

• 7+ years of professional experience in data science and machine learning, including model design, validation, and deployment.
• Strong proficiency in Python (pandas, scikit-learn, TensorFlow, PyTorch) and statistical programming (R preferred).
• Advanced knowledge of SQL and experience with big data tools such as Hive, Spark, or Pig.
• Strong understanding of machine learning techniques (e.g., SVM, Random Forests, XGBoost, Neural Networks).
• Solid foundation in statistics, probability, and mathematics (linear algebra, calculus, optimization).
• Experience with cloud data platforms (AWS, Azure, GCP) and MLOps frameworks for scalable deployment.
• Expertise in data wrangling, feature selection, and handling large, imperfect datasets.
• Excellent communication skills with the ability to explain complex concepts to both technical and non-technical stakeholders