Key Responsibilities
Extract and analyze data from company databases to drive the optimization and enhancement of product development and marketing strategies.
Analyze large datasets to uncover trends, patterns, and insights that can influence business decisions.
Leverage predictive and AI/ML modeling techniques to enhance and optimize customer experience, boost revenue generation, improve ad targeting, and more.
Design, implement, and optimize machine learning models for a wide range of applications such as predictive analytics, natural language processing, recommendation systems, and more.
Conduct experiments to fine-tune machine learning models and evaluate their performance using appropriate metrics.
Deploy machine learning models into production environments, ensuring scalability
Qualifications
Bachelor's, Master's or Ph.D in Computer Science, Data Science, Mathematics, Statistics, or a related field.
2+ years of experience in Analytics, Machine learning, Deep learning.
Proficiency in programming languages such as Python, and familiarity with machine learning libraries (e.g., Numpy, Pandas, TensorFlow, Keras, PyTorch, Scikit-learn).
Strong experience with data wrangling, cleaning, and transforming raw data into structured, usable formats.
Hands-on experience in developing, training, and deploying machine learning models for various applications (e.g., predictive analytics, recommendation systems, anomaly detection).
In-depth understanding of machine learning algorithms (supervised, unsupervised, reinforcement learning) and their appropriate use cases.
Experience with model evaluation techniques (e.g., cross-validation, A/B testing, performance metrics).
Experience with cloud platforms (AWS, GCP, Azure) for model deployment and scalal Proficiency in data processing and manipulation techniques.
Excellent problem-solving skills and the ability to work effectively in a collabor environment.
Strong communication skills to convey complex technical concepts to non-tech stakeholders.
Good To Have
Experience in the [banking/financial services/industry-specific] sector.
Familiarity with cloud-based machine learning platforms such as Azure, AWS, or GCP.
Skills: recommendation systems,pytorch,cloud,anomaly detection,numpy,python,data cleaning,deep learning,predictive analytics,a/b testing,machine learning,pandas,tensorflow,scikit-learn,data wrangling,cloud platforms (aws, gcp, azure),model evaluation techniques,cross-validation,keras,machine learning models