Data Scientist

4 days ago

Delhi, India Xander Talent Full time
Key Responsibilities Data Modelling : Perform effective data modeling and analysis to support ML applications.
Cloud-Based Data Operations : Work with cloud platforms (AWS, Azure, or GCP) to perform data analysis and modeling tasks.
Data Visualization : Create impactful dashboards and visualizations using tools like Shiny, Dash, Streamlit, PowerBI, or Tableau.
Machine Learning & Deep Learning Tools : Leverage frameworks such as H2O.ai, Spark Mllib, TensorFlow, PyTorch, Scikit-learn, XGBoost, and Stan for developing models.
Advanced Data Analysis : Apply statistical analysis, Bayesian methods, MCMC, ensemble learning, neural networks, and other advanced techniques.
Database Management : Manage data using SQL, NoSQL, Pig, Hive, and other database systems.
Scripting : Write robust scripts in Python and/or R for data processing and ML pipelines.
Distributed Computing : Utilize Big Data technologies like Hadoop, Hive, HBase, Spark, and Databricks for scalable data processing.
Natural Language Processing (NLP) : Implement NLP techniques using libraries like Spacy, NLTK, etc.
Key Requirements
Technical Skills :
Data Modelling and Analysis : Expertise in statistical methods, machine learning techniques, and advanced analytics.
Cloud Computing : Proficiency in AWS, Azure, or GCP platforms.
Visualization Tools : Experience with Shiny, Dash, Streamlit, PowerBI, or Tableau.
ML Frameworks : Strong knowledge of TensorFlow, PyTorch, Scikit-learn, and other libraries.
Big Data Technologies : Hands-on experience with Hadoop, Hive, HBase, Spark, and Databricks.
Database Management : Skilled in SQL and NoSQL databases.
Programming : Proficiency in Python and/or R scripting.
NLP : Knowledge of natural language processing techniques and tools like Spacy, NLTK, etc.
Soft Skills :
Communication Skills : Strong presentation and interpersonal communication abilities.
Analytical Mindset : Ability to analyze and comprehend complex situations and provide actionable insights.
Teamwork : Flexibility, adaptability, and a collaborative approach to working in dynamic team environments.
Preferred Qualifications
Master’s or PhD in Computer Science, Data Science, Statistics, or related fields.
Previous experience with real-world applications of machine learning in industry.

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