Senior Data Scientist

3 weeks ago

Surat, India Tatvic ® Full time
About Tatvic:

Tatvic ® is a global consulting and solutions provider that specializes in Data & Marketing Analytics. With offices across North America, APAC, and UAE, Tatvic offers end-to-end services and solutions on leading marketing and cloud platforms. They provide comprehensive marketing technology platforms and expertise in website and mobile app analytics, conversion rate optimization, and data activation for marketing.

Job description

Responsibilities of a Data Scientist

Responsibilities w.r.t Customer:

1. Keep the promises made to the customer in terms of deliverables.2. Designing solutions using data science platforms.3. Bird’s eye view of the Platform to create an effective solution4. Identify features which would matter and ensure that logic for feature selection is clear to all5. Use Auto ML as well as individual algorithms for effective solutions6. Display or export the output into a visualization platform7. Create an API which can be used to access the model for query8. Optimize the model to increase the effectiveness with proper data cleansing and feature engineering9. Create a POC for providing data insight for the customer at a short notice

Innovation and Asset building Responsibilities

1. Design and Build reusable solution which can be used for multiple customers2. Create clear documentation on architecture and design concepts used in the project3. Provide assistance to other team members to learn cloud platform by training and creating the presentation4. Maintain coding standards & Building reusable code and libraries for future use5. Stay updated with new innovations in data science.6. Explore the usage of data science in various business and web analytics applications

Technical Skills:

1. Data Handling: Manage data from diverse sources, including structured tables, unstructured text, images, videos, and streaming/real-time data. Utilize cloud platforms (preferably Google Cloud) such as BigQuery, VertexAI, and Cloud Storage for scalable data processing and analysis.

2. Feature Engineering: Identify and select relevant features with transparent and explainable logic. Design new derived features to enhance model performance and enable deeper insights. Utilize advanced techniques like Pearson Coefficient and SHAP values for feature importance and correlation analysis.

3. Model Development:

a. Select and build models based on problem requirements, using pre-trained models, AutoML, or custom algorithms. Experience in Linear, Non-linear, Timeseries (RNNs, LSTMs), Tree-based models (XGBoost, LightGBM), and other foundational approaches.

b. Apply advanced modeling techniques like CNNs for image processing, RCNNs, YOLO for object detection, and RAGs and LLM tuning for text and search-related tasks.

c. Optimize models with hyperparameter tuning, Bayesian optimization, and appropriate evaluation strategies.

4. Model Evaluation:

Assess model performance using metrics suited for data type and problem type:

a. For categorical data: Precision, Recall, F1 Score, ROC-AUC, and Precision-Recall curves.

b. For numerical data: Metrics like Mean Absolute Error (MAE), Root Mean Square Error (RMSE), Mean Squared Error (MSE), and R-squared (R²).

5. Deployment & MLOps: Deploy models for batch or real-time predictions using MLOps frameworks, leveraging tools such as VertexAI and Kubeflow for efficient and scalable deployment pipelines. Integrate outputs with visualization platforms to deliver actionable insights and drive decision-making.

6. Innovation: Stay current with trends in AI and data science, including LLMs, grounding techniques, and innovations in temporal and sequential data modeling. Regularly conduct POCs to experiment with emerging tools and technologies.

7. Code Practices & Engineering:

a. Write clean, maintainable, and scalable code following industry best practices. Adhere to version control (e.g., Git) for collaborative development and maintain coding standards.

b. Implement error handling, logging, and monitoring to ensure reliability in production systems.

c. Collaborate with other teams to integrate data science models into broader system architectures.

8. Performance Optimization: Optimize model and data processing pipelines for computational efficiency and scalability. Use parallel processing, distributed computing, and hardware accelerators (e.g., GPUs, TPUs) where applicable.

9. Documentation & Reusability: Maintain comprehensive technical documentation for all solutions. Design and build reusable assets to streamline future implementations.

Functional Competencies

- Applying Technical knowledge (SQL/no SQL/ML/DL)- Python / Libraries/Packages/ API/Web Services- Data Analysis- Cloud And Infra Services / Monitoring & Debugging/ Orchestration Frameworks- Solution/Architecture Design

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