Data Analyst- Biglittle.ai

Job Summary:

We are looking for a skilled Data Scientist with strong expertise in demand forecasting, predictive analytics, and emerging Generative AI technologies. The ideal candidate should have hands-on experience in machine learning, deep learning, NLP, and LLM-based solutions, along with proficiency in Python, SQL, Power BI, and advanced Excel. This role involves building scalable forecasting models and leveraging AI/GenAI to deliver actionable business insights.

Key Responsibilities:

• Develop and deploy demand forecasting models using machine learning and deep learning techniques.
• Analyze historical data to identify trends, seasonality, and demand patterns.
• Build predictive models to improve supply chain and inventory planning.
• Work with large datasets using Python and SQL for data extraction, transformation, and analysis.
• Design dashboards and reports using Power BI for business stakeholders.
• Utilize advanced Excel techniques (Pivot Tables, Power Query, formulas) for analysis and reporting.
• Build and integrate NLP-based solutions for text data analysis and insights.
• Develop and implement LLM-based applications using Generative AI frameworks.
• Design and deploy RAG (Retrieval-Augmented Generation) pipelines for intelligent data retrieval and response generation.
• Collaborate with cross-functional teams (operations, finance, product) to align forecasting and AI solutions.
• Continuously improve model accuracy and performance through experimentation and optimization.

Required Skills:

• Strong proficiency in Python (Pandas, NumPy, Scikit-learn, TensorFlow/PyTorch).
• Solid understanding of machine learning & deep learning algorithms.
• Experience in demand forecasting / time-series analysis (ARIMA, Prophet, LSTM, etc.).
• Hands-on experience with NLP techniques and libraries (NLTK, SpaCy, Transformers).
• Experience working with LLMs and Generative AI frameworks (OpenAI, Hugging Face, LangChain, etc.).
• Strong understanding of RAG architectures and vector databases (FAISS, Pinecone, etc.).
• Advanced knowledge of SQL for data manipulation.
• Hands-on experience with Power BI for visualization and reporting.
• Expertise in advanced Excel (Power Query, dashboards, data modeling).
• Strong analytical and problem-solving skills. 

Preferred Qualifications:

• Experience in supply chain, logistics, or e-commerce forecasting.
• Knowledge of cloud platforms (AWS, Azure, or GCP).
• Familiarity with data pipelines and ETL processes.
• Understanding of business metrics and KPIs related to demand planning.

Job Description:

As a Data Science Intern, you will collaborate with our data science and analytics teams to work on meaningful projects involving data analysis, predictive modeling, and statistical modeling. You will have the opportunity to apply your academic knowledge in a practical, fast-paced environment, contribute to key data-driven projects, and gain valuable experience with industry-leading tools and technologies.

Responsibilities:

• Assist in collecting, cleaning, and preprocessing data from various sources.
• Perform exploratory data analysis to identify trends, patterns, and anomalies.
• Develop and implement machine learning models and algorithms.
• Create data visualizations and reports to communicate findings to stakeholders.
• Collaborate with team members on data-driven projects and research.
• Participate in meetings and contribute to discussions on project progress and strategy.
• Work with large datasets to clean, preprocess, and analyze data.
• Build and deploy statistical and machine learning models to generate actionable insights.
• Conduct exploratory data analysis (EDA) to uncover trends, patterns, and correlations.
• Assist in the creation of data visualizations and dashboards for reporting insights.
• Support the development and improvement of data pipelines and algorithms.
• Collaborate with cross-functional teams to understand data needs and translate them into actionable analytics solutions.
• Contribute to the documentation and presentation of results, findings, and recommendations.
• Participate in team meetings, brainstorming sessions, and project discussions.

Duration: 03 Months (with the possibility of extending up to 6 months)

MODE: Work From Home (Online)

Requirements:

• Any Graduate / PassOuts / Freasher can apply.
• Currently pursuing a Bachelor's or Master’s degree in Data Science, Computer Science, Mathematics, Statistics, or a related field.
• Proficiency in programming languages such as Python, R, or SQL.
• Strong foundation in statistics, probability, and data analysis techniques.

Benefits

Internship Certificate

Letter of recommendation

Stipend Performance Based

Part time work from home (2-3 Hrs per day)

5 days a week, Fully Flexible Shift

About the Company: 

WebMD Health Corp., an Internet Brands Company, is the leading provider of health information services, serving patients, physicians, health care professionals, employers, and health plans through our public and private online portals, mobile platforms, and health-focused publications. The WebMD Health Network includes WebMD Health, Medscape, Jobson Healthcare Information, prIME Oncology, MediQuality, Frontline, QxMD, Vitals Consumer Services, MedicineNet, eMedicineHealth, RxList, OnHealth, Medscape Education, and other owned WebMD sites. WebMD®, Medscape®, CME Circle®, Medpulse®, eMedicine®, MedicineNet®, theheart.org®, and RxList® are among the trademarks of WebMD Health Corp. or its subsidiaries. All qualified applicants will receive consideration for employment without regard to race, color, religion, sex, sexual orientation, gender identity, national origin, disability, or veteran status.

For Company details, visit our website: www.webmd.com

Experience: 3+ years

Qualification: Master's degree in Computer Science, Statistics, Mathematics, or a related field.

Work Timings: 2 PM to 11 PM IST

Position Requirement:

• Write clean and efficient code in Python and/or R for data analysis, statistical modeling, and machine learning tasks.
• Apply statistical and machine learning techniques to analyze large datasets and extract meaningful patterns, trends, and insights. Develop and implement predictive models for various business applications.
• Formulate hypothesis tests and design experiments to validate assumptions, measure the impact of changes, and optimize decision-making processes.
• Demonstrate strong SQL skills for efficient data querying and manipulation using window functions.
• Work with large datasets and ensure data quality and accuracy.
• Experience with dashboarding tools like Tableau is a plus to visualize and present data-driven insights to both technical and non-technical stakeholders.
• Ability to communicate complex technical concepts and insights in a clear and concise manner to both technical and non-technical audiences. Engage with the leadership team and clients to understand business requirements and provide data-driven solutions.
• Work with real world case studies in Data Science and a chance to implement various modelling techniques. Opportunity to independently execute and lead analytical projects and assignments.
• Help solve some challenging Healthcare related digital marketing problems globally. Transform business question into data requirements; collect and merge the data; analyse the data, link it to the business reality and present the results
• Develop predictive models and machine learning algorithms to study change in physician prescribing behaviour as well as WebMD integrated campaign response behaviour. Build analysis to understand user engagement and behaviours across various WebMD products.

Technical Skills needed:

1.Experience with data manipulation in SQL environment is a must. Experience with BigQuery/Vertica data warehousing is good to have

• Experience with statistical and data manipulation tools such as Python or R is a must 
• Need very good expertise in using Microsoft Excel, and Microsoft PowerPoint. Excellent presentation skills required
• Experience developing statistical models like hypothesis testing, regression models, classifications models, forecasting etc is a must 
• Algorithm designing and implementation skills in R or Python is required 

Requirements

• B.Tech/B.E. / MSc Statistics from premier institutes with
• Minimum 80% marks in 10th and 12th
• Minimum 75% marks in graduation
• 2-5 years of relevant work experience in analytics field
• Proven experience in data science, statistical modeling, and machine learning applications.
• Strong proficiency in Python and/or R programming languages.
• Solid understanding of statistical concepts, hypothesis testing, and experimental design
• Expertise in SQL for data querying and manipulation.
• Familiarity with dashboarding tools is a plus.
• Excellent presentation, communication and interpersonal skills.

Role Overview 

As a Data Scientist, you will work with business stakeholders, AI engineers, and domain experts to transform data into actionable insights and intelligent solutions. You will develop machine learning models, perform statistical analysis, and contribute to AI-driven products that create measurable business impact. 

Key Responsibilities 

Data Science & Machine Learning 

• Analyze structured and unstructured data to identify patterns, trends, and business opportunities.  
• Perform exploratory data analysis (EDA), feature engineering, and data preparation.  
• Develop, evaluate, and optimize machine learning models for prediction, classification, clustering, and forecasting.  
• Apply statistical techniques to solve business problems and validate model performance.  
• Design and execute experiments to improve model accuracy and business outcomes.  

 AI Solution Development 

• Collaborate with AI Engineers, Data Engineers, and domain experts to build AI-powered solutions.  
• Translate business requirements into scalable data science approaches.  
• Contribute to Generative AI and advanced analytics initiatives where applicable.  
• Document methodologies, model performance, and key findings.  

 Required Technical Skills 

• Strong programming skills in Python and SQL for data analysis, feature engineering, and machine learning.  
• Strong understanding of Statistics, Probability, Linear Algebra, and Calculus as applied to machine learning and data science.  
• Experience with Exploratory Data Analysis (EDA), data preprocessing, feature engineering, feature selection, and handling missing or imbalanced data.  
• Good understanding of Supervised, Unsupervised, and Ensemble Machine Learning algorithms, including their assumptions, strengths, limitations, and appropriate use cases.  
• Strong knowledge of Regression, Classification, Clustering, Time Series Forecasting, Dimensionality Reduction, Recommendation Systems, and Anomaly Detection techniques.  
• Experience with Model Evaluation, Cross-Validation, Hyperparameter Optimization, Bias-Variance Trade-off, Feature Importance, Explainable AI (XAI), and Performance Metrics.  
• Understanding of Statistical Inference, Hypothesis Testing, Probability Distributions, Sampling Techniques, Confidence Intervals, and A/B Testing.  
• Experience translating business problems into analytical approaches and developing scalable, data-driven solutions.  
• Working knowledge of Generative AI, Large Language Models (LLMs), Prompt Engineering, and Retrieval-Augmented Generation (RAG) is preferred. 

Preferred Qualifications 

• Bachelor's or master's degree in computer science, Artificial Intelligence, Data Science, Statistics, Mathematics, Engineering, or a related field.  
• 2–4 years of experience developing machine learning or data science solutions.  
• Experience working on end-to-end data science projects in a business environment.  

 Nice to Have 

• Exposure to Generative AI, LLMs, RAG, or Agentic AI.  
• Experience with Computer Vision or Natural Language Processing (NLP).  
• Familiarity with cloud-based AI platforms.  
• Knowledge of construction, engineering, manufacturing, or industrial domains.  
• Participation in hackathons, research, Kaggle competitions, or open-source projects.  

 Soft Skills 

Strong analytical and problem-solving skills, effective communication and collaboration, ownership mindset, adaptability, continuous learning, and a passion for innovation.

Strong Data Analyst Profile with Python, SQL, and data-pipeline expertise, ideally with retail / e-commerce datasets

2

Mandatory (Experience 1): Must have 4+ years of hands-on data-analyst experience, working with large datasets across areas such as sales, inventory, pricing, promotions, and customer behaviour.

3

Mandatory (Tech skill 1): Must have strong hands-on Python for data processing, transformation, automation, and analysis

4

Mandatory (Tech skill 2): Must have strong SQL skills, including complex queries, joins, aggregations, and data validation.

5

Mandatory (Tech skill 3): Must have a solid understanding of Data Lake architecture, ingestion, storage, and processing workflows

6

Mandatory (Tech skill 4): Must have hands-on experience with ETL / ELT pipelines, data integration, and transformation

7

Mandatory (Tech skill 5): Must have experience with Azure or GCP data services.

8

Mandatory (Tech skill 6): Must have understanding of or experience with Databricks and distributed data processing.

9

Mandatory (Tech skill 7): Must have knowledge of data modeling, data warehousing, and Lakehouse architecture

10

Mandatory (Tech skill 8): Must have experience with Power BI, Tableau, or Excel for reporting.

11

Mandatory (Communication): Must have strong analytical, documentation, and stakeholder-communication skills.

12

Mandatory (Education): Must hold a Bachelor’s degree in Computer Science, IT, Engineering, or a related field.

13

Mandatory (Note1): This is a 6-month contract role. Candidate must be comfortable with the contract engagement and able to join immediately (urgent — onboarding within ~a week)

About Us

Corporate Web Solutions works on technology-driven digital solutions involving data, automation, web technologies, and artificial intelligence. Our internship programs focus on practical learning and real-world project exposure.

Role Overview

As a Data Science Intern, you'll work with datasets to perform analysis, visualization, and machine learning tasks while learning modern AI-assisted workflows.

Key Responsibilities

• Collect, clean, and analyze datasets.
• Perform exploratory data analysis.
• Create data visualizations and reports.
• Assist in developing machine learning models.
• Work with Python-based data science tools.
• Explore AI tools for data analysis and productivity.

Requirements

• Basic knowledge of Python.
• Understanding of data analysis fundamentals.
• Familiarity with Pandas and NumPy is a plus.
• Basic understanding of statistics.
• Analytical and problem-solving skills.

Perks

• Certificate of Internship
• Flexible work hours
• Mentorship and real project exposure
• Potential for PPO
• Letter of Recommendation
• Performance-Based Stipend available up to ₹18,000/month

Data Science Engineer – Python, SQL, Statistics & Machine Learning

Company: Talview

Experience: 3–5 years

Location: Bengaluru, Karnataka

Work Mode: Work from Office, with some flexibility

Work Week: 5 days

About Talview

Talview builds AI-powered hiring and assessment solutions for enterprise organisations. Our products support candidate screening, interview scheduling and online exam proctoring, helping teams improve efficiency and make better-informed decisions.

Role Overview

We are looking for a Data Science Engineer with strong hands-on experience in Python, SQL, statistics and classical machine learning, along with experience working on GenAI and Large Language Models (LLMs).

You will work across data processing, statistical analysis, experimentation, model development, evaluation and production AI/ML workflows. You will collaborate with Engineering and Product teams to translate business problems into measurable data science solutions.

This role requires practical experience in classical ML and statistics, beyond working only with LLM APIs or prompt engineering.

Roles and Responsibilities

• Develop complex SQL queries for data extraction, transformation, analysis and validation.
• Use Python for data processing, exploratory data analysis, automation and reusable experimentation utilities.
• Apply statistical analysis, hypothesis testing and experimental design to evaluate business and product outcomes.
• Develop and evaluate machine learning models using appropriate metrics and clearly defined success criteria.
• Build LLM workflows involving prompting, structured outputs and comparative evaluation of models and configurations.
• Create evaluation datasets, benchmarks, sampling strategies and ground-truth validation approaches.
• Perform error analysis and root-cause analysis to identify model failures, anomalies and performance degradation.
• Analyse production data and troubleshoot data, model and pipeline issues.
• Integrate data from APIs, databases and other structured and unstructured sources.
• Collaborate with Engineering and Product teams on ML pipelines, evaluation frameworks and inference workflows.
• Research and test techniques to improve AI/ML system performance.

Required Skills and Experience

• 3–5 years of relevant experience in Data Science, Machine Learning, AI or a related field.
• Strong hands-on Python programming skills.
• Strong SQL skills for complex querying, data transformation and analysis.
• Solid understanding of statistics, hypothesis testing, experiment design and performance measurement.
• Practical experience developing and evaluating classical machine learning models.
• Experience with LLMs, prompting, structured outputs and evaluation.
• Experience defining evaluation metrics, benchmarks and error-analysis approaches.
• Ability to work with APIs, databases and structured and unstructured datasets.
• Strong analytical and problem-solving skills.
• Ability to explain technical decisions, interpret results and communicate findings to stakeholders.

Preferred Skills

• Experience designing end-to-end ML or LLM workflows.
• Experience with dataset creation, sampling, annotation and ground-truth validation.
• Experience analysing production AI/ML systems and debugging model or pipeline issues.
• Experience building reusable experimentation and evaluation frameworks.

Key Skills

Python, SQL, Data Science, Statistics, Machine Learning, Classical ML, Hypothesis Testing, Experimental Design, Model Evaluation, Exploratory Data Analysis, GenAI, Large Language Models, LLM Evaluation, Error Analysis