Data Scientist

We are seeking a motivated and results-driven Junior Data Scientist with a passion for problem-solving, data analytics, and natural language processing (NLP). In this role, you will leverage your skills in Python, SQL, Machine learning, and NLP to develop data-driven solutions that drive business decisions and insights. You will be part of a dynamic team and work closely with senior data scientists and business stakeholders to create and optimize models, perform data analysis, and automate processes. As a Junior Data Scientist, you will have the opportunity to work on a variety of projects, ranging from predictive modelling to text analysis and cloud-based data solutions. You should have a solid foundation in logical thinking, problem-solving, and the ability to apply your knowledge to real-world challenges. Roles & Responsibilities - Work independently to manage multiple projects at once while ensuring deadlines are met and data output is accurate and appropriate for the business. - Implementation of newer algorithms according to the business needs. - Collaborate with cross functional agile teams of software engineers, product managers, and others in building new product features. - Think strategically about data as a core enterprise asset and assist in all phases of the advanced analytic development process. Key Technical Skills: - Programming: - Strong proficiency in Python, with experience in libraries like pandas, numpy, scikit-learn, networkx, and TensorFlow for data manipulation and machine learning tasks. - Proficient in building and deploying high-performance APIs using FastAPI and Python, ensuring fast response times and scalability. - Familiarity with Linux/Unix/Shell environments. - Experience with Data Structures and Algorithms. - Machine Learning: - Hands-on experience with supervised learning (e.g., regression, classification), unsupervised learning (e.g., clustering, PCA), and ensemble methods (e.g., random forests, gradient boosting). - Familiarity with model evaluation metrics (e.g., accuracy, precision, recall, F1-score, ROC-AUC) and techniques for hyperparameter tuning (e.g., GridSearchCV, Randomized SearchCV). - Natural Language Processing (NLP): - Understanding of key NLP tasks like text classification, sentiment analysis, named entity recognition (NER), and text summarization using traditional techniques (e.g., TF-IDF, bag-of-words) and pre-trained models. - Experience with using LLMs such as GPT, BERT, and T5 for text generation, question answering, and document classification, using libraries like Hugging Face Transformers. - Ability to fine-tune pre-trained LLMs on domain-specific data and evaluate model performance using metrics like accuracy, F1-score, bleu score, and perplexity, optimizing models for production use. - SQL & Database Management: - Strong SQL skills for querying relational databases (e.g., PostgreSQL, MySQL) and managing large datasets. - Experience in writing complex queries for data extraction, transformation, and aggregation. - Azure Cloud Services: - Familiarity with Azure Databricks, Azure Blob Storage, and Azure SQL Database for data processing and storage. - Basic knowledge of Azure Data Factory for building and orchestrating data pipelines. - Data Visualization: - Experience creating visualizations with matplotlib, seaborn, and plotly for data exploration and model results presentation. - Familiarity with dashboarding tools like Power BI or Tableau (optional). - Git: - Proficient in creating and managing feature branches, resolving merge conflicts, and merging code into the main branch using Git merge and Git rebase. - Experienced with pull requests (PRs), reviewing and commenting on code, and working within Git workflows (e.g., GitFlow or feature branching) to ensure smooth team collaboration. Required Qualifications: - A bachelor's or master's degree in computer science, statistics, mathematics, or a related field. - 3-5 years of professional experience as a Data Scientist, working with machine learning, data analysis, NLP, and cloud technologies. - Strong communication skills, with the ability to explain complex data science concepts to non-technical stakeholders. - Ability to work independently and as part of a team in a fast-paced environment. Nice-to-Have Skills: - Experience with model deployment and CI/CD pipelines. - Familiarity with big data technologies such as Spark or Hadoop. - Experience with Docker for containerization and Kubernetes for model orchestration.