Data Science Job in Techishubhdeep IT Solutions Pvt. Ltd.

Responsibilities:

Data Collection and Cleaning: Gather, preprocess, and clean large volumes of structured and unstructured data from various sources to ensure accuracy and reliability.

Data Exploration and Visualization: Explore and analyze data using statistical methods and visualization techniques to identify patterns, trends, and insights.

Machine Learning Modeling: Develop and implement machine learning models for predictive analytics, classification, clustering, and other data-driven tasks.

Feature Engineering: Engineer features from raw data to improve model performance and accuracy.

Model Evaluation and Validation: Evaluate model performance using appropriate metrics and techniques, and validate models to ensure robustness and generalizability.

Deployment and Integration: Deploy machine learning models into production systems and integrate them with existing software or platforms.

Collaboration and Communication: Collaborate with cross-functional teams including data engineers, business analysts, and stakeholders to understand business requirements and communicate findings effectively.

Continuous Learning and Improvement: Stay updated on the latest developments in data science, machine learning algorithms, and tools, and continuously improve skills and techniques.

Experience Required:

Technical Skills: Proficiency in programming languages such as Python or R, along with knowledge of libraries and frameworks like TensorFlow, PyTorch, scikit-learn, or Spark for data manipulation, analysis, and modeling.

Machine Learning Experience: Hands-on experience with machine learning algorithms and techniques, including supervised and unsupervised learning, deep learning, and natural language processing (NLP).

Data Handling: Experience working with large-scale datasets, data preprocessing, and feature engineering techniques.

Model Development: Experience in developing, training, and evaluating machine learning models using appropriate methodologies and techniques.

Software Engineering: Understanding of software development principles and practices, including version control, code optimization, and deployment pipelines.

Problem-Solving Skills: Strong analytical and problem-solving skills to address complex data science challenges and develop innovative solutions.

Qualifications Required:

Education: Typically, a bachelor's or master's degree in computer science, data science, statistics, mathematics, or a related field. Some roles may require a Ph.D. for advanced research positions.

Mathematical Foundation: Strong mathematical background with knowledge of linear algebra, calculus, probability, and statistics.

Data Science Courses/Certifications: Completion of relevant data science courses, certifications, or bootcamps can supplement formal education and demonstrate proficiency in key skills and techniques.

Portfolio: A strong portfolio showcasing projects, competitions, or research in data science, machine learning, or related fields can provide evidence of practical experience and expertise.

Communication Skills: Effective communication skills to articulate technical concepts and findings to both technical and non-technical stakeholders.

By fulfilling these requirements, data scientists can effectively leverage data to derive insights, solve complex problems, and drive business value within organizations.