Posted:2 days ago|
Platform:
Work from Office
Full Time
Core Technical Skills:
• Programming Languages: Advanced skills in Python (e.g., Pandas, NumPy, Matplotlib) and R for data analysis and modeling; familiarity with SQL for querying large datasets.• ML Frameworks: Proficiency with Scikit-learn for traditional ML, TensorFlow or PyTorch for deep learning, and XGBoost or LightGBM for high-performance predictive models.• Data Tools: Experience with big data platforms like Apache Spark or Hadoop, and cloud-based data services (e.g., AWS Redshift, Azure Data Lake, GCP BigQuery).• Cloud Platforms: Hands-on knowledge of AWS (e.g., S3, SageMaker), Azure (e.g., Synapse Analytics), or GCP (e.g., Vertex AI) for scalable data processing and model hosting.• Visualization Tools: Expertise in Tableau, Power BI, or Matplotlib/Seaborn to craft impactful data stories.• Generative AI: Exposure to models like BERT, GPT, or Hugging Face Transformers for NLP tasks or synthetic data generation in finance.• MLOps Basics: Familiarity with Docker for containerization and Git for version control in collaborative projects.
Required Experience:
• 6+ years of experience in data science, preferably in the finance industry.• Strong proficiency in Python, R, SQL, and machine learning libraries (e.g., Scikit-learn, TensorFlow).• Experience in statistical analysis, data modeling, and deep learning.• Familiarity with cloud-based data platforms (e.g., AWS, Azure, GCP).• Strong communication skills for presenting findings to stakeholders.
Key Responsibilities:
• Analyze large financial datasets to identify trends and patterns.• Develop and deploy machine learning models for predictive analytics and decision support.• Work with product teams to understand requirements and develop data-driven solutions.• Translate business requirements into machine learning tasks.• Create visualizations to communicate model results to non-technical stakeholders.• Stay up-to-date with emerging AI/ML techniques and tools.
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