AI/ML Data Scientist – Time Series Forecasting & GenAI

Job Title: AI/ML Data Scientist – Time Series Forecasting & GenAI

Experience: 4–7 Years

Relevant Hands-on Experience: 1–3 Years in AI/ML & Data Science

Location: PAN India

Employment Type: Contract / Project-Based

Openings: 2

Job Summary

We are looking for an experienced AI/ML Data Scientist with hands-on expertise in Time Series Forecasting, Python, SQL, PySpark, Statistical Modeling, Machine Learning, and Generative AI .

The ideal candidate will have experience building scalable data-processing pipelines, developing forecasting models, performing feature engineering, and integrating LLM-based solutions to generate business insights.

The role involves working with large datasets on Google Cloud Platform , developing production-ready machine learning solutions, and combining traditional forecasting techniques with modern Generative AI / LLM capabilities .

Key Responsibilities

Data Engineering & Feature Engineering

- Build scalable data-processing pipelines using PySpark / PySpark ML .
- Work with Google Cloud DataProc for distributed data processing.
- Perform data cleansing, transformation, aggregation, and validation.
- Develop time-window aggregations and KPI calculations.
- Create meaningful features for machine learning and forecasting models.
- Perform feature selection and feature-importance analysis.
- Work with large-scale structured and time-series datasets.

Time Series Forecasting
- Develop and implement time-series forecasting solutions for business use cases.
- Build forecasting models using:
- ARIMA
- SARIMA
- SARIMAX
- XGBoost
- Analyze historical trends, seasonality, patterns, and anomalies.
- Evaluate forecasting model performance using appropriate statistical and business metrics.
- Tune models and improve forecast accuracy.
- Compare multiple forecasting approaches and select appropriate methodologies based on business requirements.
- Develop reusable forecasting pipelines for production environments.

Machine Learning
- Develop machine learning models using Python and Scikit-learn .
- Perform exploratory data analysis and statistical analysis.
- Conduct feature engineering and feature selection.
- Evaluate model performance and conduct model validation.




- Apply appropriate statistical and machine learning techniques to solve business problems.
- Collaborate with engineering teams to deploy and operationalize ML solutions.

Generative AI / LLM
- Integrate Gemini LLM into data science and business intelligence solutions.
- Develop LLM-powered reasoning and insight-generation workflows.
- Use LangChain for developing GenAI applications and workflows.
- Develop effective prompts using Prompt Engineering techniques.
- Combine traditional forecasting outputs with LLM-based reasoning.
- Generate actionable business insights from forecasting results and data.
- Evaluate and improve the quality, consistency, and reliability of LLM-generated outputs.

GCP / Data Platform
- Work with Google Cloud DataProc for distributed data processing.
- Work with BigQuery for data storage, querying, and analytics.
- Utilize relevant GCP Data & AI services.
- Build scalable data-processing and ML workflows on GCP.
- Collaborate with cloud and data engineering teams to operationalize solutions.

Mandatory Technical Skills Candidates must have hands-on experience with the following:

Programming & Data

- Python – Mandatory
- SQL – Mandatory
- PySpark
- PySpark ML
- Data processing and transformation
- Feature engineering

Machine Learning & Statistics
- Scikit-learn
- Statistical modeling
- Machine learning
- Time-series analysis
- Feature selection
- Feature importance

Time Series Forecasting
- ARIMA
- SARIMA
- SARIMAX
- XGBoost
- Forecasting model development and evaluation

Google Cloud
- Google Cloud DataProc
- BigQuery
- GCP Data & AI services

Generative AI
- Gemini LLM
- LangChain
- Prompt Engineering
- LLM-based reasoning
- GenAI application development

Required Experience
- 4–7 years of total professional experience
- 1–3 years of relevant hands-on AI/ML & Data Science experience




- Strong practical experience in Python and SQL.
- Experience working with large datasets and distributed processing.
- Hands-on experience with time-series forecasting.
- Experience implementing statistical and machine learning models.
- Experience working with GCP data services.
- Practical experience integrating LLM/GenAI capabilities into data science solutions.

Preferred Experience Experience in any of the following will be an advantage:
- Production ML pipelines
- MLOps
- GCP Vertex AI
- Data visualization / BI
- Model monitoring
- Forecasting at scale
- GenAI-powered analytics
- LLM evaluation
- Advanced prompt engineering
- Cloud-based machine learning platforms

Candidate Profile The ideal candidate should be:
- Strong in analytical and problem-solving skills.
- Comfortable working independently on complex data science requirements.
- Able to translate business requirements into data science solutions.
- Comfortable working with both traditional ML/statistical models and GenAI.
- Strong in communicating technical concepts to engineering and business stakeholders.
- Comfortable working in a cooperative, production-focused environment.

Interview Process

Round 1: Customer Interview

Round 2: 1–2 Subsequent Client Interview Rounds

Application

Interested candidates can share their updated CV with the following details:

- Total Experience
- Relevant AI/ML Experience
- Python Experience
- PySpark Experience
- Time-Series Forecasting Experience
- ARIMA/SARIMA/SARIMAX/XGBoost Experience
- GCP DataProc & BigQuery Experience
- Gemini / LangChain Experience
- Current Location
- Notice Period / Availability
- Current CTC & Expected CTC

? Email: [email protected] ? WhatsApp: +91 92848 05350

LinkedIn Recruiter Search Keywords

AI/ML Data Scientist, Data Scientist, Machine Learning Engineer, AI Engineer, Python, SQL, PySpark, PySpark ML, Scikit-learn, Time Series Forecasting, Time Series Analysis, ARIMA, SARIMA, SARIMAX, XGBoost, Statistical Modeling, Machine Learning, Google Cloud DataProc, GCP, BigQuery, Gemini, Gemini LLM, LangChain, Generative AI, GenAI, Prompt Engineering, LLM, Feature Engineering, Feature Selection, Forecasting Models, GCP Data & AI.

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