Preferred Candidate Profile

- 4+ years of hands-on experience in Data Science, Machine Learning, or Advanced Analytics.
- Strong programming skills in Python with practical experience in data analysis and machine learning.
- Strong understanding of machine learning algorithms, statistics, and predictive modeling.
- Good hands-on experience with SQL and working with structured and large datasets.
- Experience in data preprocessing, EDA, feature engineering, model building, validation, and optimization.
- Should have experience handling Data Science projects end-to-end, from understanding the problem to developing and evaluating the solution.
- Strong analytical and problem-solving abilities.
- Ability to translate business problems into data-driven solutions.
- Good communication and stakeholder management skills.
- Comfortable working independently and collaboratively in a fully remote environment.
- Candidates with exposure to Deep Learning, NLP, Generative AI, Cloud, or MLOps will be preferred.

Skills

Mandatory Skills:

- Data Science
- Python
- Machine Learning
- SQL
- Statistics & Probability
- Predictive Modeling
- Exploratory Data Analysis (EDA)
- Feature Engineering
- Model Evaluation & Optimization
- Scikit-learn
- Pandas
- NumPy

Positive to Have:

- Deep Learning TensorFlow / PyTorch
- NLP
- Generative AI / LLMs
- Time Series Forecasting




- MLOps / ML Model Deployment
- MLflow
- Docker
- AWS / Azure / GCP
- Power BI / Tableau

Role & Responsibilities

- Analyze large and complex datasets to identify trends, patterns, and actionable business insights.
- Develop, train, validate, and optimize machine learning and predictive models.
- Perform data cleaning, preprocessing, exploratory data analysis (EDA), and feature engineering.
- Apply statistical and machine learning techniques to solve business problems.
- Build classification, regression, clustering, forecasting, and other predictive models as required.
- Use Python and SQL for data analysis, manipulation, and model development.
- Evaluate model performance using appropriate statistical and machine learning metrics.
- Work with cross-functional teams to understand business requirements and convert them into data-driven solutions.
- Present analytical findings and model results to technical and non-technical stakeholders.
- Continuously improve existing models and analytical solutions based on new data and business requirements.
- Maintain clear documentation of data science methodologies, models, experiments, and results.
- Contribute to the deployment and productionization of machine learning solutions where required.

📌 Data Scientist (India)
🏢 Talent Corner HR Services
📍 India