Data Analyst - NLP/Machine Learning (6-9 yrs)

Job Description :

Roles and Responsibilities :

- The Ideal candidate would act as Senior Consulting Engineer for Data Science and AI, will be responsible for doing design and development work on a daily basis, including but not restricted to Development, Deployment and Post Support of solutions using the entire Data and AI suite of products.

- Design system architectures to meet the product needs and business scale both in the short and the long term.

- Own the High-Level Design of systems and Low-Level Design of the domain in line with the industry standard practices to ensure ease of maintenance and iteration

- Set goals for platform evolution, and the roadmap for achieving it.

- Propose and drive technical innovation and experiments.

- A minimum of 6-10 years of experience in Product Software Development is required.

- Core competencies should include but not limited to Machine Learning, Deep Learning, Natural Language Processing, Data Mining, Data Engineering, Data Analysis & Data Visualization.

- Expertise in programming in Java or Python.

- Experience with various NLP/ML libraries in Python is an added advantage.

- Design and Develop Content Moderation Models : Create and train models to automatically detect offensive language, hate speech, bullying, and other violations in user-generated content (UGC).

- Implement Sentiment Analysis : Develop and refine models to gauge sentiment across user posts, comments, and reviews (e.g., positive, negative, joy, anger) to understand user engagement and flag potential issues.

- Build Image and Video Moderation Models : Collaborate with computer vision specialists to detect explicit content in multimedia using models for nudity, violence, weapons, etc.

- Monitor and Improve ACM Performance : Continuously evaluate moderation model effectiveness, minimize false positives/negatives, and update models as new types of content or trends emerge.

- Collaborative Filtering & Content-Based Modeling : Develop personalized recommendation algorithms for flights, hotels, vacation rentals, etc., using collaborative filtering and content-based techniques.

- Implement Hybrid Recommendation Systems : Combine multiple algorithms (e.g., matrix factorization, neural networks) for improved personalization.

- A/B Testing and Personalization : Design experiments to validate the effectiveness of recommendations and improve user engagement metrics.

- Optimize Real-Time Recommendation Delivery : Work with engineering to ensure recommendations are served in real time, meeting performance and scalability requirements.

- Statistical Analysis and Predictive Modeling : Perform statistical analysis to uncover trends in booking behaviors, pricing, seasonal trends, and user preferences.

- User Behavior Analysis : Track and analyze user journeys across the platform, identifying opportunities for product enhancement and improved customer experience.

- Develop Dashboards and Reports : Provide actionable insights by building dashboards and reports for stakeholders using BI tools like Tableau or Power BI.

- Market Segmentation and User Profiling : Implement clustering and classification techniques to segment users and tailor product offerings.

- Collaborate with Data Engineering : Work closely with data engineers to establish scalable data pipelines for structured and unstructured data collection, preprocessing, and storage.

- Model Deployment and Maintenance : Collaborate with ML Ops to deploy and monitor models in production environments, ensuring robustness and adaptability.

- Data Quality and Compliance : Ensure data privacy, security, and regulatory compliance, especially with UGC and PII (Personally Identifiable Information).

- Demand Forecasting : Build time series models to forecast demand for different regions, seasons, and services (e.g., hotels, flights).

- Churn Prediction and Customer Retention Models : Identify at-risk users and offer insights for targeted engagement strategies.

- Basic database / SQL experience.

- A minimum of 3-5 years of experience in database application programming (SQL, PL/SQL and SQL PL).

- Experience building software for SaaS and Continuous Delivery Techniques is desirable.

- Demonstrates verbal and written communications skills.

- Demonstrates strong analytical thinking and problem-solving skills.

- Creative approach to problem solving, innovation and issue resolution.

- Superior interpersonal skills and the ability to collaborate actively and proactively with others in a cross-functional team.

Predictive Insights for Inventory Management: Work with business teams to forecast inventory needs based on demand patterns, booking trends, and external factors.

Preferred Technical and Professional Expertise :

- Machine Learning experience

- Chatbots and NLP Application development experience

- Demonstrates proactive communication skills and has collaboration experience.

- Knowledge of the agile software development cycle

- Knowledge of Online travel application/industry is an asset

- Exceptional attention to detail, and a commitment to delivering under tight time constraints

Mandatory Skills :

- Statistical Analysis and Hypothesis Testing: Proficiency in statistical methods, hypothesis testing, and experiment design (e.g., A/B testing).

- Machine Learning: Solid understanding of algorithms including classification, clustering, regression, and deep learning; experience with frameworks like Scikit-Learn, TensorFlow, or PyTorch.

- NLP and Sentiment Analysis : Strong skills in Natural Language Processing (NLP) for text analysis, sentiment analysis, and content moderation tasks, using libraries such as NLTK, SpaCy, or transformers like BERT.

- Recommendation Algorithms : Familiarity with collaborative filtering, content-based filtering, matrix factorization, and hybrid recommendation systems.

- Time Series Analysis : Experience with time series forecasting techniques like ARIMA, Prophet, or LSTM for demand and price prediction.

- Data Preprocessing and ETL Pipelines : Proficiency in data wrangling, cleaning, and transformation; experience with tools like Apache Spark, Airflow, or NiFi is a plus.

- Database Knowledge : Strong understanding of SQL and familiarity with NoSQL databases (e.g., MongoDB, Elasticsearch) for handling large-scale UGC.

- Big Data and Cloud Platforms : Experience working with big data technologies (Hadoop, Hive) and cloud environments (AWS, GCP, or Azure).

- Data Visualization Tools : Proficiency with visualization tools such as Tableau, Power BI, or matplotlib/seaborn for presenting insights.

- Dashboard Development : Experience building and maintaining data dashboards and reports for cross-functional teams.

- Model Deployment and ML Ops : Knowledge of deploying models in production environments, using Docker, Kubernetes, or ML Ops tools like MLflow or TFX.

- APIs and Real-Time Data Processing : Experience building APIs to serve models or integrate recommendations; knowledge of Kafka or RabbitMQ for streaming data.

- Problem Solving and Critical Thinking : Ability to translate business needs into data science problems and develop effective solutions.

- Collaboration and Communication : Strong communication skills for collaborating with cross-functional teams, including engineering, product, and marketing teams, and the ability to explain complex models to non-technical stakeholders.