Required Skills:

<ul>
<li style="margin-left:0px;">Minimum 5 years in a Data Science or ML role with direct experience in NLP and forecasting.</li>
</ul>

<ul>
<li style="margin-left:0px;">Proficient in Python and libraries like Pandas, NumPy, Scikit-learn, HuggingFace Transformers, and Prophet/ARIMA.</li>
</ul>

<ul>
<li style="margin-left:0px;">Strong understanding of model development lifecycle: from data ingestion to deployment.</li>
</ul>

<ul>
<li style="margin-left:0px;">Hands-on experience with SQL and data visualization (e.g., Seaborn, Matplotlib, Tableau).</li>
</ul>

<ul>
<li style="margin-left:0px;">Experience handling retail-specific data (ideally Apparel/Footwear) with seasonality, inventory, and sales nuances.</li>
</ul>

<ul>
<li style="margin-left:0px;">Familiarity with cloud platforms like AWS, GCP, or Azure for scalable model training and deployment.</li>
</ul>

Good to have:

<ul>
<li style="margin-left:0px;">Experience or interest in Computer Vision: object detection, OCR, or visual product search.</li>
</ul>

<ul>
<li style="margin-left:0px;">Familiarity with OpenCV, YOLO, CNNs, or OCR tools like Tesseract, EasyOCR.</li>
</ul>

<ul>
<li style="margin-left:0px;">Exposure to multi-modal ML systems (combining text, image, and tabular data).</li>
</ul>

<ul>
<li style="margin-left:0px;">Knowledge of API development (FastAPI, Flask) to expose ML models for product use.</li>
</ul>

<ul>
<li style="margin-left:0px;">Understanding of MLOps practices (e.g., CI/CD for ML, model monitoring).</li>
</ul>

<ul>
<li style="margin-left:0px;">Previous experience in fine-tuning LLMs or applying transfer learning in domain-specific NLP tasks.</li>
</ul>

Data Engineering:

<ul>
<li style="margin-left:0px;">Experience in building and managing data pipelines using Azure Data Factory.</li>
</ul>

<ul>
<li style="margin-left:0px;">Familiarity with Azure Synapse Analytics for data warehousing and analytics.</li>
</ul>

<ul>
<li style="margin-left:0px;">Working knowledge of Azure Data Lake, Databricks on Azure, or Azure SQL for handling large-scale data.</li>
</ul>

<ul>
<li style="margin-left:0px;">Good understanding of ETL/ELT processes, data transformation, and integration workflows in Azure ecosystem.</li>
</ul>

<ul>
<li style="margin-left:0px;">Experience with scheduling, monitoring, and optimizing pipelines for ML data readiness.</li>
</ul>

Key Responsibilities: Natural Language Processing:

<ul>
<li style="margin-left:0px;">Apply NLP techniques to extract insights from text data such as product descriptions, reviews, internal documents, and reports.</li>
</ul>

<ul>
<li style="margin-left:0px;">Fine-tune transformer-based models (e.g., BERT, RoBERTa, LLaMA) for classification, summarization, and named entity recognition.</li>
</ul>

<ul>
<li style="margin-left:0px;">Work with domain experts to create and maintain retail-specific taxonomies and knowledge structures.</li>
</ul>

<ul>
<li style="margin-left:0px;">Ensure model outcomes are explainable, ethical, and aligned with business needs.</li>
</ul>

Time Series Forecasting:

<ul>
<li style="margin-left:0px;">Analyze historical demand, inventory, and sales data to uncover trends and seasonality.</li>
</ul>

<ul>
<li style="margin-left:0px;">Utilize pre-trained models (e.g., ARIMA, Prophet, DeepAR) to generate accurate demand forecasts.</li>
</ul>

<ul>
<li style="margin-left:0px;">Fine-tune and adapt existing models using domain-specific data from the apparel/footwear industry.</li>
</ul>

<ul>
<li style="margin-left:0px;">Translate forecasts into actionable insights for supply chain, merchandising, and planning teams.</li>
</ul>

Good to have - Computer Vision:

<ul>
<li style="margin-left:0px;">Assist or contribute to CV projects involving product imagery, shelf monitoring, visual search, or quality control.</li>
</ul>

<ul>
<li style="margin-left:0px;">Apply basic CV techniques like image classification, object detection (e.g., using YOLO, OpenCV, or CNNs).</li>
</ul>

<ul>
<li style="margin-left:0px;">Support integration of image-based data into larger multi-modal ML systems.</li>
</ul>

Collaboration &amp; Integration:

<ul>
<li style="margin-left:0px;">Work closely with cross-functional teams (e.g., Data Engineering, DevOps, Product, and Business Stakeholders).</li>
</ul>

<ul>
<li style="margin-left:0px;">Develop scalable, reusable ML components that can be deployed in production environments.</li>
</ul>

<ul>
<li style="margin-left:0px;">Participate in designing evaluation frameworks and A/B tests for forecasting and NLP model effectiveness. </li>
</ul>
Qualifications 
<ul>
<li style="margin-left:0px;">Master's degree in Computer Science, Data Science, Statistics, or a related field</li>
</ul>

<ul>
<li style="margin-left:0px;">Proven experience in data science, machine learning, or a similar role</li>
</ul>

<ul>
<li style="margin-left:0px;">Strong proficiency in Python and SQL for data manipulation and analysis</li>
</ul>

<ul>
<li style="margin-left:0px;">Experience with cloud technologies, preferably Azure Databricks and MLflow</li>
</ul>

<ul>
<li style="margin-left:0px;">In-depth knowledge of machine learning algorithms, including regression, classification, clustering, and deep learning</li>
</ul>

<ul>
<li style="margin-left:0px;">Expertise in feature engineering, feature selection, and model evaluation techniques</li>
</ul>

<ul>
<li style="margin-left:0px;">Strong skills in exploratory data analysis (EDA) and data visualization</li>
</ul>

<ul>
<li style="margin-left:0px;">Experience building end-to-end AI systems and pipelines</li>
</ul>

<ul>
<li style="margin-left:0px;">Familiarity with big data technologies such as Hadoop and Spark</li>
</ul>

<ul>
<li style="margin-left:0px;">Solid understanding of statistical analysis and probability theory</li>
</ul>

<ul>
<li style="margin-left:0px;">Experience with version control systems, preferably Git</li>
</ul>

<ul>
<li style="margin-left:0px;">Excellent problem-solving skills and ability to translate complex business problems into technical solutions</li>
</ul>

<ul>
<li style="margin-left:0px;">Strong communication skills to effectively present findings to both technical and non-technical audiences</li>
</ul>

<ul>
<li style="margin-left:0px;">Ability to work independently and collaboratively in a fast-paced, dynamic environment</li>
</ul>

<ul>
<li style="margin-left:0px;">Knowledge of distributed systems is a plus</li>
</ul>

Data Science

India

Required Skills: Minimum 5 years in a Data Science or ML role with direct experience in NLP and forecasting. Proficient in Python and libraries like Pandas, NumPy, Scikit-learn, HuggingFace Transformers, and Prophet/ARIMA. Strong understanding of model development lifecycle: from data ingestion to deployment. Hands-on experience with SQL and data visualization (e.g., Seaborn, Matplotlib, Tableau). Experience handling retail-specific data (ideally Apparel/Footwear) with seasonality, inventory, and sales nuances. Familiarity with cloud platforms like AWS, GCP, or Azure for scalable model training and deployment. Good to have: Experience or interest in Computer Vision: object detection, OCR, or visual product search. Familiarity with OpenCV, YOLO, CNNs, or OCR tools like Tesseract, EasyOCR. Exposure to multi-modal ML systems (combining text, image, and tabular data). Knowledge of API development (FastAPI, Flask) to expose ML models for product use. Understanding of MLOps practices (e.g., CI/CD for ML, model monitoring). Previous experience in fine-tuning LLMs or applying transfer learning in domain-specific NLP tasks. Data Engineering: Experience in building and managing data pipelines using Azure Data Factory. Familiarity with Azure Synapse Analytics for data warehousing and analytics. Working knowledge of Azure Data Lake, Databricks on Azure, or Azure SQL for handling large-scale data. Good understanding of ETL/ELT processes, data transformation, and integration workflows in Azure ecosystem. Experience with scheduling, monitoring, and optimizing pipelines for ML data readiness. Key Responsibilities: Natural Language Processing: Apply NLP techniques to extract insights from text data such as product descriptions, reviews, internal documents, and reports. Fine-tune transformer-based models (e.g., BERT, RoBERTa, LLaMA) for classification, summarization, and named entity recognition. Work with domain experts to create and maintain retail-specific taxonomies and knowledge structures. Ensure model outcomes are explainable, ethical, and aligned with business needs. Time Series Forecasting: Analyze historical demand, inventory, and sales data to uncover trends and seasonality. Utilize pre-trained models (e.g., ARIMA, Prophet, DeepAR) to generate accurate demand forecasts. Fine-tune and adapt existing models using domain-specific data from the apparel/footwear industry. Translate forecasts into actionable insights for supply chain, merchandising, and planning teams. Good to have - Computer Vision: Assist or contribute to CV projects involving product imagery, shelf monitoring, visual search, or quality control. Apply basic CV techniques like image classification, object detection (e.g., using YOLO, OpenCV, or CNNs). Support integration of image-based data into larger multi-modal ML systems. Collaboration &amp; Integration: Work closely with cross-functional teams (e.g., Data Engineering, DevOps, Product, and Business Stakeholders). Develop scalable, reusable ML components that can be deployed in production environments. Participate in designing evaluation frameworks and A/B tests for forecasting and NLP model effectiveness. Qualifications Master's degree in Computer Science, Data Science, Statistics, or a related field Proven experience in data science, machine learning, or a similar role Strong proficiency in Python and SQL for data manipulation and analysis Experience with cloud technologies, preferably Azure Databricks and MLflow In-depth knowledge of machine learning algorithms, including regression, classification, clustering, and deep learning Expertise in feature engineering, feature selection, and model evaluation techniques Strong skills in exploratory data analysis (EDA) and data visualization Experience building end-to-end AI systems and pipelines Familiarity with big data technologies such as Hadoop and Spark Solid understanding of statistical analysis and probability theory Experience with version control systems, preferably Git Excellent problem-solving skills and ability to translate complex business problems into technical solutions Strong communication skills to effectively present findings to both technical and non-technical audiences Ability to work independently and collaboratively in a fast-paced, dynamic environment Knowledge of distributed systems is a plus

Data Science · Chennai

Job highlights

Skillset required

Job Description

Job Description for Data Science

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