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.