Data Scientist
We are looking for a talented and analytical Data Scientist with 2–5 years of experience in building data-driven solutions, developing machine learning models, and extracting actionable insights from complex datasets. The ideal candidate should have strong expertise in Python, SQL, statistics, machine learning, and data visualization, along with the ability to solve real-world business problems using data. Key Responsibilities Collect, clean, preprocess, and analyze structured and unstructured datasets. Develop, train, evaluate, and deploy machine learning models for predictive and prescriptive analytics. Perform exploratory data analysis (EDA) to identify trends, patterns, and insights. Build statistical models and apply advanced analytical techniques to solve business challenges. Collaborate with product, engineering, and business teams to translate requirements into data-driven solutions. Design and execute A/B testing and experimentation frameworks. Develop dashboards and reports to communicate findings to stakeholders. Optimize model performance through feature engineering, hyperparameter tuning, and model evaluation. Monitor deployed models and ensure their performance, reliability, and scalability. Document methodologies, experiments, and model performance for future reference. Required Skills 2–5 years of experience as a Data Scientist. Strong proficiency in Python and SQL . Hands-on experience with machine learning libraries such as Scikit-learn , XGBoost , or LightGBM . Experience with data manipulation libraries like Pandas and NumPy . Strong understanding of statistics, probability, and hypothesis testing. Experience with data visualization tools such as Matplotlib , Seaborn , Plotly , Power BI , or Tableau . Familiarity with supervised and unsupervised learning algorithms. Experience with feature engineering, model evaluation, and model deployment. Understanding of version control tools such as Git . Strong analytical thinking, problem-solving, and communication skills. Preferred Skills Experience with deep learning frameworks such as TensorFlow or PyTorch . Exposure to cloud platforms such as AWS , Azure , or Google Cloud Platform (GCP) . Familiarity with MLOps tools such as MLflow , Kubeflow , or SageMaker . Knowledge of big data technologies such as Apache Spark or PySpark . Experience working with NLP, computer vision, recommendation systems, or Generative AI is an added advantage. Understanding of Docker, Kubernetes, and CI/CD pipelines is a plus. Eligibility Criteria Experience: 2–5 years Graduation Year: 2023 or earlier Strong communication and stakeholder management skills. Ability to work independently and collaboratively in a fast-paced environment.