Lead Data Scientist

Posted 2024-10-23

💎 Seniority level: Lead, 5+ years

📍 Location: India

🔍 Industry: Global Capability Centers (GCC)

🏢 Company: InOrg Global

⏳ Experience: 5+ years

🪄 Skills: LeadershipProject ManagementPythonKerasMachine LearningPyTorchData scienceTensorflowCollaboration

Master’s or PhD in a relevant field, with a strong emphasis on Machine Learning, Deep Learning, and AI.
5+ years of experience in data science, including leadership roles.
Proficient in Python; knowledge of BI, Snowflake, and Snowpark is advantageous.

Lead the design and implementation of sophisticated models using Machine Learning and Deep Learning techniques.
Oversee the delivery of data science projects, ensuring they meet business requirements and are completed on schedule.
Mentor and develop team members, enhancing their skills in Python, Machine Learning, Deep Learning, and AI.
Collaborate with stakeholders across the organization to identify opportunities for leveraging data to drive business solutions.

Posted 2024-10-15

📍 India

🔍 Product engineering, cloud-native, data engineering, B2B SaaS, IoT & Machine Learning

5+ years of industry experience in data science or related field.
Strong expertise in statistical analysis, predictive modeling, and machine learning algorithms.
Proficient in Python and popular data analysis and machine learning libraries (e.g., Pandas, NumPy, Scikit-learn, TensorFlow, PyTorch).
Experience with big data processing frameworks (e.g., Spark, Hadoop).
Solid understanding of SQL and databases.
Experience with cloud platforms (e.g., AWS, Azure, GCP).
Excellent problem-solving and analytical skills.
Strong communication and presentation skills.
Master's or PhD in a quantitative field (e.g., Data Science, Computer Science, Statistics, Mathematics) is preferred.

Collaborate with cross-functional teams to define problem statements, hypotheses, and success metrics for data science projects.
Design, develop and deploy machine learning models and predictive analytics solutions at scale.
Perform exploratory data analysis, feature engineering, and data preprocessing to support model development.
Apply statistical analysis and machine learning techniques to extract insights and solve business problems.
Prototype and implement data products and pipelines using modern data tools and frameworks.
Drive the adoption of best practices in data science, including experiment design, testing, and evaluation.
Communicate findings and insights to stakeholders through data visualization, reports, and presentations.
Stay up-to-date with the latest developments and trends in machine learning and data science.

AWSPythonSQLData AnalysisGCPHadoopMachine LearningNumpyPyTorchAlgorithmsAzureData analysisData sciencePandasSparkTensorflowAnalytical Skills

Posted 2024-10-15

🔧 Requirements