Data Science Trainer

Need 5+ Years experience

The role of a Data Scientist combines statistical analysis, machine learning, data processing, and domain expertise to extract valuable insights from data and solve complex problems.
- **Data Gathering**: Collect and acquire large datasets from various internal and external sources, such as databases, APIs, and third-party services.
- **Data Cleaning**: Handle missing values, remove outliers, and correct inaccuracies in the data to ensure it is accurate, reliable, and ready for analysis.
- **Data Transformation**: Preprocess and transform raw data into formats suitable for analysis. This could involve normalization, scaling, encoding, and other techniques.
- **Data Exploration**: Analyze the dataset to understand its structure, identify trends, patterns, and correlations, and detect any anomalies.
- **Visualizations**: Create data visualizations (using tools like Matplotlib, Seaborn, or Tableau) to help understand the relationships in the data and communicate insights to non-technical stakeholders.
- **Model Building**: Develop machine learning models to predict outcomes, classify data, or uncover hidden insights. This includes supervised learning, unsupervised learning, and deep learning methods.
- **Algorithm Selection**: Choose the appropriate machine learning algorithms (e.g., regression, classification, clustering, neural networks) based on the problem at hand.
- **Model Training**: Train models using historical data and optimize them for accuracy and performance, adjusting hyperparameters as necessary.
- **Model Validation & Testing**: Validate and test the models using techniques like cross-validation, holdout sets, and performance metrics (e.g., precision, recall, F1 score).
- **Model Deployment**: Work with engineering teams to deploy machine learning models into production systems for real-time or batch predictions.
- **Model Monitoring**: Continuously monitor model performance post-deployment to detect any degradation over time and retrain models with fresh data if necessary.
- **Automation**: Automate repetitive tasks, such as data collection, processing, and reporting, using scripts and workflow tools.
- **Insight Generation**: Analyze data to generate actionable insights that can inform business decisions, optimize processes, and identify opportunities.
- **Data Visualization**: Create clear, understandable data visualizations (using tools like Power BI, Tableau, or custom dashboards) to present insights to both technical and non-technical stakeholders.
- **Reporting**: Generate reports, summaries, and presentations that communicate findings effectively and help guide strategic decisions.
- **Cross-Department Collaboration**: Work closely with various teams such as engineering, marketing, product, and operations to understand business problems and provide data-driven solutions.
- **Business Understanding**: Collaborate with business stakeholders to ensure that the models and analyses align with organizational objectives and priorities.
- **Stakeholder Communication**: Clearly communicate complex analytical results to non-technical stakeholders, ensuring they understand the insights and how to act on them.
- **Keep Up with Trends**: Stay up-to-date with the latest trends and advancements in data science, machine learning, and AI technologies.
- **Methodology Improvement**: Continuously explore and experiment with new statistical and machine learning techniques to improve existing models and processes.
- **Proof-of-Concepts**: Build prototypes or proof-of-concept solutions for innovative data-driven ideas.
- **Data Privacy**: Ensure compliance with data privacy laws and guidelines (e.g., GDPR, CCPA) when handling personal and sensitive data.
- **Skills Required**:

- **Programming**: Proficiency in languages like Python, R, SQL, and possibly Java or Scala.
- **Machine Learning**: Strong understanding of machine learning algorithms, deep learning, and frameworks (e.g., TensorFlow, Keras, Scikit-learn).
- **Statistics & Mathematics**: Advanced knowledge of statistical techniques, probability theory, and hypothesis testing.
- **Data Manipulation**: Expertise in data manipulation libraries (e.g., Pandas, NumPy).
- **Data Visualization**: Experience with data visualization tools (e.g., Tableau, Power BI, Matplotlib).
- **Big Data**: Knowledge of big data technologies such as Hadoop, Spark, or cloud platforms (AWS, Google Cloud).
- **Problem Solving**: Strong analytical and critical thinking skills to solve complex problems.

Pay: ₹100,000.00 - ₹300,000.00 per month

Schedule:

- Day shift

**Experience**:

- total work: 5 years (preferred)

Work Location: In person

Application Deadline: 02/01/2025
Expected Start Date: 02/01/2025