Senior Data Scientist

Role & Responsibilities

Responsibilities

• Contribute to the development and optimization of enterprise-wide search systems and models.

• Design and implement algorithms to improve indexing, query relevance, and search accuracy.

• Support taxonomy, ontology, and metadata model creation for better search outcomes.

• Collaborate with business units (Loans, Insurance, Investments) to build AI-enabled search features.

• Conduct analysis of user behavior and system metrics to refine search performance.

• Work with engineers, product managers, and designers to deliver integrated search solutions.

• Develop production-grade ML systems for ranking, personalization, and recommendations.

• Participate in proof-of-concept initiatives with internal and external partners.

• Follow best practices in software engineering including CI/CD, testing, and monitoring.

• Keep abreast of emerging developments in AI/ML to apply them in practical solutions.

Ideal Candidate

Strong Data Scientist / AI Engineer / Machine Learning Engineer profiles.

Mandatory (Experience 1) – Must have minimum 5+ years of hands-on experience in Data Science, Machine Learning, Applied AI, NLP, Deep Learning, or Generative AI solutions.

Mandatory (Experience 2) – Must have strong hands-on experience in Python programming, SQL, data analysis, feature engineering, model development, and production-grade ML applications.

Mandatory (Experience 3) – Must have experience working with Machine Learning and Deep Learning frameworks such as PyTorch, TensorFlow, Keras, Scikit-learn, or equivalent.

Mandatory (Experience 4) – Must have hands-on experience working on NLP, embeddings, semantic search, text classification, document understanding, recommendation systems, or similar AI/ML use cases.

Mandatory (Experience 5) – Must have experience working with Large Language Models (LLMs) such as GPT, Llama, Mistral, Claude, Gemini, Phi, or similar foundation models.

Mandatory (Experience 6) – Must have hands-on experience building or implementing RAG (Retrieval Augmented Generation) systems, vector search, knowledge retrieval, embeddings, chunking, indexing, or semantic retrieval solutions.

Mandatory (Experience 7) – Must have experience working with Git, CI/CD practices, production environments, and scalable AI/ML systems.

Mandatory (CTC) – The CTC breakup offered will be 75% fixed + 25% variable, as per company policy.

Mandatory (Age) - Candidate's Age should be below 30 Years

Preferred (Experience 1) – Experience with MLFlow, Kubeflow, Airflow, Prefect, Feature Stores, Model Registry, or MLOps/LLMOps frameworks.

Preferred (Experience 2) – Experience working with Vector Databases, Spark, PySpark, distributed ML pipelines, large-scale data processing, or real-time ML systems..

Preferred (Experience 3) – Familiarity with Docker, Kubernetes, Azure, AWS, GCP, cloud-native AI deployments, and scalable ML architecture.

Preferred (Company) – Candidates from AI-first startups, Fintech, Banking, Lending, Fraud Analytics, Risk Analytics, Product Companies, SaaS organizations, or data-driven technology companies.

Kindly provide the following details while sending your CV: (Mandatory details)

1) Date of Birth

2) Current Location-

3) Current CTC-

4) Expected CTC-

5) Notice Period-

6) Ready to relocate to Pune?

Regards,

The Supreme Consultancy

Website- https://lnkd.in/eawfxfxU

Job Description

Our Media Measurement team uses state-of-the-art technologies and rigorous methods to track who is watching what, where, and how they engage with content. Our clients can evaluate who is consuming which content across different media, platforms and devices, and know what the audience thinks about that content. As people consume media content on more channels, and through more devices, than ever before, we are proud to provide a full view on media consumption.

As Data Scientist you will have following main accountabilities: 

• You own together with your team several of our Data Science solutions throughout the full life cycle (brainstorming, design, implementation, productization and maintenance) 
• You develop solutions based on data science, stats and machine learning models 
• You improve methods and tools. Contribute to our communities of practice in the area of Data Science 
• Communicate with non-data scientists in Tech, Operations, Commercial, Product. Understand the domain and the requirements. Explain Data Science principles, concepts, algorithms, and approaches in simple words to different types of audiences 
• Make data your best friends. Understand their strengths and use them. Be aware of their weaknesses and handle those in your solutions 
• Screen the market for potential new Data Science approaches 
• Foster knowledge exchange within the company. Present GfK's Data Science expertise at conferences and workshops 

Qualifications

Now you know what a Data Scientist does. What skills, qualifications & experience do you need for this job? 

• You typically have a Master's degree or PhD that reflects modeling and statistics skills and 6+ years of experience. 
• You enjoy communicating complex methodology and technology to tech and non tech audiences 
• You have expert statistical / machine learning modeling skills (e.g. statistical tests, classification, predictive modelling, handling of missing data, sampling, weighting) 
• You have experience with an analytical programming language (Python) and the respective ecosystem 

Besides the things we really expect you to have, there are some things which would be amazing if you have experience with them: 

• Knowledge of cloud computing environments and tooling (especially AWS) 
• Advanced software development skills (unit testing, CI/CD, Git) 
• Basic skills regarding database handling as SQL 
• Basic knowledge of the always evolving Data Science ecosystem and its frameworks 

Additional Information

• Enjoy a flexible and rewarding work environment with peer-to-peer recognition platforms. 
• Recharge and revitalize with help of wellness plans made for you and your family. 
• Plan your future with financial wellness tools. 
• Stay relevant and upskill yourself with career development opportunities. 

Our Benefits

• Flexible working environment
• Volunteer time off
• LinkedIn Learning
• Employee-Assistance-Program (EAP)

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.

Sr Engineer – Artificial Intelligence

Job Summary 

As an AI Engineer at Emerson, you will be responsible for analysing complex data sets to

identify trends, develop predictive models, and provide actionable insights. You will work closely

with cross-functional teams to understand business needs and deliver data-driven solutions that

enhance decision-making and drive business growth.

 In This Role, Your Responsibilities Will Be:

 Analyze large, complex data sets using statistical methods and machine learning

techniques to extract meaningful insights.

 Develop and implement predictive models and algorithms to solve business problems

and improve processes.

 Create visualizations and dashboards to effectively communicate findings and insights to

stakeholders.

 Work with data engineers, product managers, and other team members to understand

business requirements and deliver solutions.

 Clean and preprocess data to ensure accuracy and completeness for analysis.

 Prepare and present reports on data analysis, model performance, and key metrics to

stakeholders and management.

 Participate in regular Scrum events such as Sprint Planning, Sprint Review, and Sprint

Retrospective

 Stay updated with the latest industry trends and advancements in data science and

machine learning techniques.

For This Role, You Will Need:

 Bachelor’s degree in computer science, Data Science, Statistics, or a related field or a

master's degree or higher is preferred.

 Total 5-7 years of industry experience

 More than 3 years of experience in a data science or analytics role, with a strong track

record of building and deploying models.

 Proficiency in programming languages such as Python or R, and experience with data

manipulation libraries (e.g., pandas, NumPy).

 Excellent understanding of Agentic Frameworks like Microsoft Agent Framework.

 Experience with NLP, NLG, and Large Language Models Open Source as well as Cloud

based models.

 Experience with SQL and NoSQL databases such as MongoDB, Cassandra, Vector

databases

 Experience with Dockers, Asynchronous Data Orchestrators, environments etc.

 Strong analytical and problem-solving skills, with the ability to work with complex data

sets and extract actionable insights.

 Excellent verbal and written communication skills, with the ability to present complex

technical information to non-technical stakeholders.

Preferred Qualifications that Set You Apart:

 Prior experience in engineering domain would be nice to have

 Prior experience in working with teams in Scaled Agile Framework (SAFe) is nice to

have

 Possession of relevant certification/s in data science from reputed universities

specializing in AI.

 Familiarity with cloud platforms, Microsoft Azure is preferred

 Ability to work in a fast-paced environment and manage multiple projects simultaneously.

 Strong analytical and troubleshooting skills, with the ability to resolve issues related to

model performance and infrastructure.

Sr.Data Scientist,Python, AI ML

We are looking for a skilled Data Scientist to analyze complex datasets, develop predictive models, and generate actionable insights that support business decisions. The ideal candidate should have strong statistical, analytical, and programming skills, along with hands-on experience in machine learning.

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.

Strong Data Scientist / AI Engineer / Generative AI Engineer profile.

2

Mandatory (Experience 1) - Must have 3+ years of hands-on experience in Data Science, Artificial Intelligence, Machine Learning, Deep Learning, NLP, or Generative AI application development.

3

Mandatory (Experience 2) - Must have strong hands-on experience in Python programming, backend development, API development, and production-grade application support.

4

Mandatory (Experience 3) - Must have experience working with Machine Learning and Deep Learning frameworks such as PyTorch, TensorFlow, Keras, or Scikit-learn.

5

Mandatory (Experience 4) - Must have hands-on experience in NLP use cases such as text classification, sentiment analysis, entity recognition (NER), semantic search, embeddings, or document understanding.

6

Mandatory (Experience 5) - Must have experience working with Large Language Models (LLMs) such as GPT, LLaMA, Mistral, Phi, Claude, Gemini, or similar models.

7

Mandatory (Experience 6) - Must have hands-on experience building or implementing Retrieval Augmented Generation (RAG) solutions, vector search, semantic search, or knowledge-based AI applications.

8

Mandatory (Experience 7) - Must have experience with Prompt Engineering and Generative AI frameworks such as LangChain, LangGraph, AI Agents, Azure OpenAI, or similar technologies.

9

Mandatory (Experience 8) - Must have experience developing, consuming, or integrating APIs using Python frameworks such as FastAPI, Flask, or similar technologies.

10

Mandatory (CTC) - The CTC breakup offered will be 75% fixed + 25% variable, as per company policy.

11

Preferred (Experience 1) - Experience with LLMOps/MLOps tools for monitoring, evaluation, experimentation, and versioning of AI models.

12

Preferred (Experience 2) - Exposure to Azure OpenAI, Azure Kubernetes Service (AKS), Kubernetes, cloud-native AI deployments, or distributed systems.

13

Preferred (Experience 3) - Experience working with PostgreSQL, MongoDB, Redis, Kafka, or large-scale data platforms.

14

Preferred (Experience 4) - Familiarity with Docker, Kubernetes, cloud platforms, and scalable deployment architecture.

15

Preferred (Company) - Candidates from AI-first startups, product companies, SaaS organizations, fintech, or data-driven technology companies.

16

Mandatory ( Age ) - Candidate Should be Below 28 Years.

17

Mandatory ( Pedigree) - B.TECH / M.TECH from Tier 1 Colleges (IIT's, NIT's, BITS) are Considered.

Job Description – Data Scientist (Machine Learning & Forecasting)

About the Role

We are looking for a highly skilled Data Scientist with strong expertise in Machine Learning, Traditional Statistical Modelling, Forecasting, and Predictive Analytics. The ideal candidate will have hands-on experience building and deploying end-to-end ML solutions, working with large datasets, and translating business problems into scalable data science solutions.

The role requires a strong foundation in statistics, predictive modelling, feature engineering, model evaluation, and time-series forecasting, along with the ability to collaborate with cross-functional teams to deliver business impact.

Key Responsibilities

• Design, develop, and deploy Machine Learning models for business-critical use cases.
• Build and optimize traditional ML models such as:
• Linear Regression
• Logistic Regression
• Decision Trees
• Random Forest
• Gradient Boosting (XGBoost, LightGBM, CatBoost)
• Support Vector Machines
• Clustering Algorithms
• Develop forecasting solutions using:
• ARIMA / SARIMA
• Prophet
• Exponential Smoothing
• Time-Series Regression Models
• Perform exploratory data analysis (EDA), feature engineering, and data validation.
• Evaluate model performance using appropriate statistical and business metrics.
• Work with structured and semi-structured datasets from multiple sources.
• Collaborate with business stakeholders to understand requirements and translate them into analytical solutions.
• Build scalable data pipelines and support model deployment in production environments.
• Monitor model performance, identify data drift, and implement model retraining strategies.
• Present insights and recommendations to technical and non-technical stakeholders.

Required Skills & Qualifications

• Bachelor's or Master's degree in Computer Science, Statistics, Mathematics, Data Science, Engineering, or a related quantitative field.
• 5+ years of hands-on experience in Data Science, Machine Learning, and Forecasting.

Technical Skills

   Machine Learning

• Strong understanding of supervised and unsupervised learning algorithms.
• Experience with ensemble methods and advanced ML techniques.
• Expertise in model selection, hyperparameter tuning, and performance optimization.

Forecasting & Statistics

• Strong understanding of:
• Time-Series Analysis
• Forecasting Techniques
• Statistical Inference
• Hypothesis Testing
• Probability Distributions
• A/B Testing

Programming

• Advanced proficiency in Python.
• Experience with:
• Pandas
• NumPy
• Scikit-learn
• Statsmodels
• XGBoost / LightGBM
• Prophet

Data & SQL

• Strong SQL skills with experience in complex queries and performance optimization.
• Experience working with large-scale datasets.

Visualization

• Experience with Power BI, Tableau, Matplotlib, Seaborn, or Plotly.
• Cloud & MLOps (Preferred)
• Exposure to AWS, Azure, or GCP.
• Understanding of Docker, Kubernetes, CI/CD, and ML model deployment practices.

Key Competencies

• Strong analytical and problem-solving skills.
• Excellent communication and stakeholder management abilities.
• Ability to work independently in a fast-paced environment.
• Strong business acumen and data-driven decision-making mindset. 

Strong Data Scientist / AI Engineer / Generative AI Engineer profile.

2

Mandatory (Experience 1) - Must have 3+ years of hands-on experience in Data Science, Artificial Intelligence, Machine Learning, Deep Learning, NLP, or Generative AI application development.

3

Mandatory (Experience 2) - Must have strong hands-on experience in Python programming, backend development, API development, and production-grade application support.

4

Mandatory (Experience 3) - Must have experience working with Machine Learning and Deep Learning frameworks such as PyTorch, TensorFlow, Keras, or Scikit-learn.

5

Mandatory (Experience 4) - Must have hands-on experience in NLP use cases such as text classification, sentiment analysis, entity recognition (NER), semantic search, embeddings, or document understanding.

6

Mandatory (Experience 5) - Must have experience working with Large Language Models (LLMs) such as GPT, LLaMA, Mistral, Phi, Claude, Gemini, or similar models.

7

Mandatory (Experience 6) - Must have hands-on experience building or implementing Retrieval Augmented Generation (RAG) solutions, vector search, semantic search, or knowledge-based AI applications.

8

Mandatory (Experience 7) - Must have experience with Prompt Engineering and Generative AI frameworks such as LangChain, LangGraph, AI Agents, Azure OpenAI, or similar technologies.

9

Mandatory (Experience 8) - Must have experience developing, consuming, or integrating APIs using Python frameworks such as FastAPI, Flask, or similar technologies.

10

Mandatory (CTC) - The CTC breakup offered will be 75% fixed + 25% variable, as per company policy.

11

Preferred (Experience 1) - Experience with LLMOps/MLOps tools for monitoring, evaluation, experimentation, and versioning of AI models.

12

Preferred (Experience 2) - Exposure to Azure OpenAI, Azure Kubernetes Service (AKS), Kubernetes, cloud-native AI deployments, or distributed systems.

13

Preferred (Experience 3) - Experience working with PostgreSQL, MongoDB, Redis, Kafka, or large-scale data platforms.

14

Preferred (Experience 4) - Familiarity with Docker, Kubernetes, cloud platforms, and scalable deployment architecture.

15

Preferred (Company) - Candidates from AI-first startups, product companies, SaaS organizations, fintech, or data-driven technology companies.

16

Mandatory ( Age ) - Candidate Should be Below 28 Years.

17

Mandatory ( Pedigree) - B.TECH / M.TECH from Tier 1 Colleges (IIT's, NIT's, BITS) are Considered.

About the Role

We are looking for a highly skilled Data Scientist with strong expertise in Machine Learning, MLOps, and Generative AI. The ideal candidate will have hands-on experience in building scalable ML models, deploying them in production, and working with modern AI frameworks, including GenAI technologies.

Key Responsibilities

·      Design, develop, and deploy machine learning models for real-world business problems

·      Work on end-to-end ML lifecycle: data preprocessing, model building, evaluation, deployment, and monitoring

·      Implement and manage MLOps pipelines for scalable and reproducible workflows

·      Utilize tools like MLflow for experiment tracking, model versioning, and lifecycle management

·      Develop and integrate Generative AI (GenAI) solutions such as LLM-based applications

·      Collaborate with cross-functional teams (engineering, product, business) to translate requirements into AI solutions

·      Optimize model performance and ensure production stability

·      Stay updated with the latest advancements in AI/ML and GenAI ecosystems

Required Skills & Qualifications

·      4+ years of experience in Data Science / Machine Learning

·      Strong programming skills in Python

·      Hands-on experience with ML modeling techniques (supervised, unsupervised, NLP, etc.)

·      Solid understanding of MLOps practices and tools

·      Experience with MLflow or similar model lifecycle tools 

·      Practical experience in Generative AI (GenAI), including working with LLMs

·      Experience with libraries/frameworks like Scikit-learn, TensorFlow, PyTorch

·      Strong understanding of data structures, algorithms, and statistics

·      Experience with cloud platforms (AWS/GCP/Azure) is a plus

Good to Have

·      Experience with LLM fine-tuning, prompt engineering, or RAG pipelines

·      Exposure to Docker, Kubernetes, and CI/CD pipelines

·      Knowledge of data engineering workflows