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- Modeling complex problems, discovering insights, and identifying opportunities through the use of statistical, algorithmic, mining, and visualization techniques

- Experience working with business understanding the requirement, creating the problem statement, and building scalable and dependable Analytical solutions

- Must have hands-on and strong experience in Python

- Broad knowledge of fundamentals and state-of-the-art in NLP and machine learning

- Strong analytical & algorithm development skills

- Deep knowledge of techniques such as Linear Regression, gradient descent, Logistic Regression, Forecasting, Cluster analysis, Decision trees, Linear Optimization, Text Mining, etc

- Ability to collaborate across teams and strong interpersonal skills

Skills

- Sound theoretical knowledge in ML algorithm and their application

- Hands-on experience in statistical modeling tools such as R, Python, and SQL

- Hands-on experience in Machine learning/data science

- Strong knowledge of statistics

- Experience in advanced analytics / Statistical techniques – Regression, Decision trees, Ensemble machine learning algorithms, etc

- Experience in Natural Language Processing & Deep Learning techniques 

- Pandas, NLTK, Scikit-learn, SpaCy, Tensorflow

About us:

Hypersonix.ai is revolutionizing the e-commerce landscape by harnessing the power of AI, ML, and advanced decision capabilities to deliver real-time business insights. Built from the ground up with cutting-edge technology, Hypersonix.ai simplifies data consumption for our diverse range of customers across various industry verticals.

Roles and Responsibilities:

• Collaborate with cross-functional teams in designing, developing, and deploying traditional machine learning models and algorithms for supply chain optimization.
• Conduct research, experimentation, and implementation of state-of-the-art traditional machine learning techniques and frameworks to address complex challenges.
• Develop and enhance forecasting models for demand prediction, inventory management, and pricing optimization.
• Optimize traditional machine learning models for tasks such as inventory forecasting, pricing strategies, and demand forecasting.
• Stay abreast of the latest advancements in traditional machine learning, forecasting techniques, and optimization methods, integrating them into our projects.
• Collaborate closely with data scientists, software engineers, and product teams to seamlessly integrate machine learning solutions into production environments.
• Document research findings, methodologies, and codebase for effective knowledge sharing and team collaboration.
• Troubleshoot and resolve issues in production environments to ensure system reliability and performance.
• Conduct root cause analysis of product defects and implement effective solutions.
• Design, develop, and maintain components of the product to drive customer adoption.
• Utilize various data science methodologies to tackle complex business problems effectively.

Qualifications:

• Strong problem-solving skills and the ability to tackle complex, open-ended challenges.
• Self-motivated individual with a strong work ethic, capable of working independently and collaboratively within a team.
• Proven experience in traditional machine learning, forecasting, pricing, and inventory optimization with a strong portfolio of projects (7-9 years).
• Experience working on NLP and deep learning.
• Proficiency in Python programming and the ability to write efficient, maintainable code.
• Expertise in traditional machine learning libraries and frameworks such as scikit-learn, XGBoost, and LightGBM.
• Experience with cloud-based AI services and infrastructure (e.g., AWS).
• Demonstrated experience in API development and integration.
• Previous experience working in production environments, ensuring system stability and performance.

About us: Nexopay helps transforming digital payments and enabling instant financing for parents, across schools and colleges world-wide.

Responsibilities:

• Work with stakeholders throughout the organisation and across entities to identify opportunities for leveraging internal and external data to drive business impact
• Mine and analyze data to improve and optimise performance, capture meaningful insights and turn them into business advantages
• Assess the effectiveness and accuracy of new data sources and data gathering techniques
• Develop custom data models and algorithms to apply to data sets
• Use predictive modeling to predict outcomes and identify key drivers
• Coordinate with different functional teams to implement models and monitor outcomes
• Develop processes and tools to monitor and analyze model performance and data accuracy

Requirements:

• Experience in solving business problem using descriptive analytics, statistical modelling / machine learning
• 2+ years of strong working knowledge of SQL language
• Experience with visualization tools e. g., Tableau, Power BI
• Working knowledge on handling analytical projects end to end using industry standard tools (e. g., R, Python)
• Strong presentation and communication skills
• Experience in education sector is a plus
• Fluency in English

• Use data to develop machine learning models that optimize decision making in Credit Risk, Fraud, Marketing, and Operations
• Implement data pipelines, new features, and algorithms that are critical to our production models
• Create scalable strategies to deploy and execute your models
• Write well designed, testable, efficient code
• Identify valuable data sources and automate collection processes.
• Undertake to preprocess of structured and unstructured data.
• Analyze large amounts of information to discover trends and patterns.

Requirements:

• 1+ years of experience in applied data science or engineering with a focus on machine learning
• Python expertise with good knowledge of machine learning libraries, tools, techniques, and frameworks (e.g. pandas, sklearn, xgboost, lightgbm, logistic regression, random forest classifier, gradient boosting regressor etc)
• strong quantitative and programming skills with a product-driven sensibility