Data Scientist

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.

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.

Hiring for Data Scientist / Senior Data Scientist

Exp : 4 - 12 yrs

Edu : BE/B.tech/MCA

Work Location : Pune

Notice Period : Immediate - 15 days

Skills :

4+ years of experience in data engineering, data science, or related domains.

Hands-on experience with SQL, Python, and distributed data systems.

Knowledge of machine learning techniques and statistical analysis.

Experience with cloud data platforms (Azure Data Factory, AWS Glue, GCP BigQuery).

Familiarity with DevOps practices and CI/CD for data pipelines.

Platforms & Operations Experience (Preferred)

- Experience working with Azure, AWS, or Google Cloud data tools.

Operational experience with data orchestration tools (Airflow, ADF, Glue).

Understanding of Kubernetes, Docker, or containerized environments.

Hands-on experience with data warehousing platforms (Snowflake, Redshift, BigQuery).

Experience in monitoring, logging, and alerting operations for data workflows.

🚀 We’re Hiring | Data Scientist 🧠📊

Ready to turn data into real-world intelligence? Join us and work on exciting AI/ML & data-driven solutions!

🔹 Experience: 8+ Years

🔹 Must-Have Skills:

🐍 Python | 🤖 Machine Learning | ☁️ Cloud | 🧠 NLP | 📊 Data Visualization

📍 Location: Pune

💼 Work Mode: Work from Office

If you're passionate about Data Science, AI & solving complex business problems, we’d love to hear from you!

📩 Interested? Kindly text

#Hiring #DataScientist #DataScience #MachineLearning #Python #NLP #AI #Cloud #DataVisualization #TechJobs #HiringNow

We are looking for a hands-on Lead Data Scientist with strong analytical, machine learning, and problem-solving skills to work in a client-facing environment.

The role requires someone who can independently identify opportunities, formulate hypotheses, design solutions, and drive initiatives from analysis through experimentation and production implementation. The candidate should also be comfortable guiding team members and working closely with engineering and client stakeholders.

Responsibilities: 

• Analyse complex datasets to identify patterns, issues, and opportunities.
• Formulate and validate hypotheses through structured analysis and experimentation.
• Build and improve machine learning and anomaly detection solutions.
• Design end-to-end solutions considering data, modelling, engineering, and production constraints.
• Perform root cause analysis across models, data, and systems.
• Work with engineering teams on feature pipelines, model inference, and production deployment.
• Lead technical discussions with clients and communicate recommendations, trade-offs, and expected impact.
• Guide team members on analysis, modelling, and solution design.
• Proactively identify initiatives and roadmap items that can add value to the project.

Requirements: 

• Strong foundation in statistics and machine learning.
• Strong hands-on experience with Python and SQL.
• Hands-on experience with Python ML and data frameworks such as Pandas, NumPy, scikit-learn, TensorFlow/Keras, Dask, Matplotlib, Boto3 SageMaker Python SDK, and Horovod.
• Strong data analysis, hypothesis-generation, and problem-solving skills.
• Experience with standard supervised and unsupervised ML techniques.
• Experience with anomaly detection techniques such as Isolation Forest and Autoencoders.
• Experience building and deploying production ML solutions.
• Working knowledge of data engineering and real-time / batch inference environments.
• Strong communication and stakeholder-management skills.
• Ability to lead technical work and guide cross-functional teams.

Good to Have: 

• Experience in fraud detection or risk modelling.
• Experience with Graph Neural Networks.
• Exposure to real-time systems, streaming features, and low-latency data stores.
• Experience in AdTech, e-commerce, gaming, mobile applications, or similar high-volume consumer platforms.

Digital Mold Data Scientist and Manufacturing Analytics Specialist

Job Summary

COAST Systems is seeking a Data Scientist and Manufacturing Analytics Specialist in India to support a global client’s Digital Mold program.

This position will work with large and complex manufacturing, engineering, tooling, maintenance, and quality datasets. The successful candidate will transform fragmented operational data into reliable datasets, dashboards, actionable insights, and continuous-improvement opportunities.

This is a hands-on analytics role requiring close collaboration with engineering, manufacturing, operations, and business stakeholders. The position is particularly suited to someone who can understand a technical manufacturing problem, determine what the data is showing, and communicate practical recommendations that improve performance.

Key Responsibilities

• Collect, prepare, cleanse, normalize, and validate manufacturing and engineering data from multiple sources.
• Establish reliable and repeatable datasets for analytics, reporting, and decision-making.
• Analyze data to identify trends, risks, performance gaps, improvement opportunities, and potential cost savings.
• Develop and maintain dashboards, visualizations, KPI reporting, and business intelligence solutions.
• Analyze maintenance and operational performance using measures such as:
• Mean Time to Repair or MTTR
• Mean Time Between Failures or MTBF
• Overall Equipment Effectiveness or OEE
• Preventive and corrective maintenance performance
• Tool reliability, condition, quality, utilization, and lifecycle indicators
• Work closely with engineering and operations teams to convert technical and operational problems into data-driven solutions.
• Support continuous improvement, operational excellence, and process optimization initiatives.
• Align analyses and recommendations with client goals, priorities, and expected business outcomes.
• Identify relationships among tooling, maintenance, production, quality, sensor, and lifecycle data.
• Present findings clearly to technical and nontechnical stakeholders.
• Help establish consistent data definitions, analytical methods, and reporting standards.
• Support the development of predictive analytics, machine learning, and AI-enabled capabilities where appropriate.
• Learn the COAST software environment and relevant client or third-party systems.
• Help map and connect tool-specific data across systems so that information can be aligned and used consistently.

Required Qualifications

• Bachelor’s or master’s degree in data science, statistics, mathematics, computer science, engineering, operations research, or a related quantitative discipline.
• Strong data science and analytics experience involving large, complex, or multi-source datasets.
• Demonstrated experience with data preparation, cleansing, transformation, normalization, validation, and data-quality management.
• Strong statistical and analytical problem-solving skills.
• Proven experience developing dashboards, data visualizations, KPI reporting, and business intelligence solutions.
• Ability to analyze data and translate findings into clear, practical business or operational recommendations.
• Experience working with technical, engineering, operational, or business stakeholders.
• Strong continuous-improvement and process-optimization mindset.
• Ability to communicate clearly in English with global teams and client stakeholders.
• Ability to work independently, manage priorities, and investigate unclear or incomplete data.
• Strong attention to detail and commitment to data accuracy.

Preferred Qualifications

• Experience analyzing data in a manufacturing, engineering, maintenance, operations, or asset-management environment.
• Understanding of manufacturing equipment, tooling, maintenance, quality, and asset lifecycle concepts.
• Familiarity with MTTR, MTBF, OEE, preventive maintenance, reliability, and related manufacturing KPIs.
• Experience in plastics manufacturing or packaging, including any of the following:
• Injection molding
• Blow molding
• Extrusion blow molding
• Injection stretch blow molding
• Compression molding
• Other polymer-processing operations
• Experience working with cloud-based data platforms or data lake environments.
• Experience combining data from multiple software systems, databases, APIs, files, or vendor platforms.
• Experience with sensor, machine, equipment, IoT, or time-series data.
• Exposure to predictive analytics, machine learning, anomaly detection, forecasting, or AI applications.
• Experience developing analytics that lead to actionable workflows, reduced costs, improved reliability, or reduced manual effort.
• Experience supporting global organizations or working in a client-facing environment.

Technical Skills

Candidates should demonstrate proficiency in several of the following areas:

• SQL
• Python or R
• Statistical analysis
• Data preparation and transformation
• Data validation and data-quality analysis
• Dashboard and visualization development
• Power BI, Tableau, QuickSight, or a comparable BI platform
• Cloud data lakes or cloud analytics environments
• Relational and non-relational data sources
• Advanced Microsoft Excel
• Predictive modeling or machine learning
• API or multi-system data integration

Specific experience with every listed technology is not required. The candidate must, however, have strong foundational analytics skills and the ability to learn unfamiliar platforms and data environments.

Critical Competencies

• Analytical curiosity
• Structured problem-solving
• Systems thinking
• Data accuracy and attention to detail
• Continuous-improvement mindset
• Business and operational awareness
• Clear written and verbal communication
• Cross-functional collaboration
• Client responsiveness
• Adaptability and willingness to learn
• Ability to convert analysis into action

Experience

Approximately 4 to 8 years of relevant professional experience is preferred. Candidates with fewer years may be considered if they demonstrate strong hands-on analytics experience, manufacturing exposure, and the ability to work directly with engineering and operational stakeholders.

What Success Looks Like

The successful candidate will:

• Create trusted and repeatable manufacturing datasets.
• Deliver dashboards and reports that stakeholders actively use.
• Identify meaningful risks, trends, and improvement opportunities.
• Help engineering and operations teams make better decisions from their data.
• Improve the consistency of tool-specific information across systems.
• Progressively develop more advanced predictive and AI-enabled Digital Mold capabilities.
• Produce measurable improvements in reliability, operational performance, cost, and efficiency.

Suggested Key Skills

Data Science, Manufacturing Analytics, Data Analytics, Business Intelligence, Dashboard Development, Data Visualization, Power BI, Tableau, Amazon QuickSight, SQL, Python, R, Statistical Analysis, Data Cleansing, Data Normalization, Data Validation, Data Quality, Manufacturing KPI, OEE, MTTR, MTBF, Predictive Analytics, Machine Learning, Continuous Improvement, Process Optimization, Maintenance Analytics, Reliability Analytics, Cloud Data Lake, Sensor Data, IoT Analytics, Injection Molding, Plastics Manufacturing

Description

We’re seeking a highly skilled, execution-focused Senior Data Scientist with a minimum of 5 years of experience. This role demands hands-on expertise in building, deploying, and optimizing machine learning models at scale, while working with big data technologies and modern cloud platforms. You will be responsible for driving data-driven solutions from experimentation to production, leveraging advanced tools and frameworks across Python, SQL, Spark, and AWS. The role requires strong technical depth, problem-solving ability, and ownership in delivering business impact through data science.

Responsibilities

• Design, build, and deploy scalable machine learning models into production systems.
• Develop advanced analytics and predictive models using Python, SQL, and popular ML/DL frameworks (Pandas, Scikit-learn, TensorFlow, PyTorch).
• Leverage Databricks, Apache Spark, and Hadoop for large-scale data processing and model training.
• Implement workflows and pipelines using Airflow and AWS EMR for automation and orchestration.
• Collaborate with engineering teams to integrate models into cloud-based applications on AWS.
• Optimize query performance, storage usage, and data pipelines for efficiency.
• Conduct end-to-end experiments, including data preprocessing, feature engineering, model training, validation, and deployment.
• Drive initiatives independently with high ownership and accountability.
• Stay up to date with industry best practices in machine learning, big data, and cloud-native deployments.

Requirements

• Minimum 5 years of experience in Data Science or Applied Machine Learning.
• Strong proficiency in Python, SQL, and ML libraries (Pandas, Scikit-learn, TensorFlow, PyTorch).
• Proven expertise in deploying ML models into production systems.
• Experience with big data platforms (Hadoop, Spark) and distributed data processing.
• Hands-on experience with Databricks, Airflow, and AWS EMR.
• Strong knowledge of AWS cloud services (S3, Lambda, SageMaker, EC2, etc.).
• Solid understanding of query optimization, storage systems, and data pipelines.
• Excellent problem-solving skills, with the ability to design scalable solutions.
• Strong communication and collaboration skills to work in cross-functional teams.

Benefits

• Best-in-class salary: We hire strong talent and compensate accordingly.
• Proximity Talks: Meet and learn from designers, engineers, product leaders, and AI practitioners.
• Continuous learning: Work with a world-class team and stay close to the latest in AI, engineering, and product development.
• High-impact work: Build AI-first systems and products used at scale by global clients.

About Us

Proximity is the trusted technology, design, and consulting partner for some of the biggest Sports, Media, and Entertainment companies in the world. We’re headquartered in San Francisco and have offices in Palo Alto, Dubai, Mumbai, and Bangalore.

Since 2019, Proximity has built high-impact, scalable products used by millions of users every day. Today, we are a global team of engineers, designers, product managers, and experts solving complex problems and building cutting-edge technology at scale.

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. 

About VerbaFlo.ai:

VerbaFlo.ai is a fast-growing AI SaaS startup revolutionizing how businesses leverage AI-powered solutions. As a part of our dynamic team, you’ll work alongside industry leaders and visionaries to drive innovation and execution across multiple functions.

Role Overview:

We are seeking a Senior Business Data Analyst with strong experience in analytics, SQL, and dashboarding (preferably Metabase) who can independently lead complex analytical initiatives, translate business problems into scalable data solutions, and mentor junior analysts. This role demands someone who can think strategically, operate with high ownership, and ensure the company runs on accurate, timely, and actionable insights.

Responsibilities:

Strategic & Cross-Functional Ownership

• Partner with leadership (Product, Ops, Growth, Finance) to translate business goals into analytical frameworks, KPIs, and measurable outcomes.
• Influence strategic decisions by providing data-driven recommendations, forecasting, and scenario modeling.
• Drive adoption of data-first practices across teams and proactively identify high-impact opportunity areas.

Analytics & Dashboarding

• Own end-to-end development of dashboards and analytics systems in Metabase (or similar BI tools).
• Build scalable KPI frameworks, business reports, and automated insights to support day-to-day and long-term decision-making.
• Ensure data availability, accuracy, and reliability across reporting layers.

Advanced Data Analysis

• Write, optimize, and review complex SQL queries for deep-dives, cohort analysis, funnel performance, and product/operations diagnostics.
• Conduct root-cause analysis, hypothesis testing, and generate actionable insights with clear recommendations.

Data Infrastructure Collaboration

• Work closely with engineering/data teams to define data requirements, improve data models, and support robust pipelines.
• Identify data quality issues, define fixes, and ensure consistency across systems and sources.

Leadership & Process Excellence

• Mentor junior analysts, review their work, and establish best practices across analytics.
• Standardize reporting processes, create documentation, and improve analytical efficiency.

Core Requirements:

• 5–8 years of experience as a Business Analyst, Data Analyst, Product Analyst, or similar role in a fast-paced environment.
• Strong proficiency in SQL, relational databases, and building scalable dashboards (Metabase preferred)
• Demonstrated experience in converting raw data into structured analysis, insights, and business recommendations.
• Strong understanding of product funnels, operational metrics, and business workflows.
• Ability to communicate complex analytical findings to both technical and non-technical stakeholders.
• Proven track record of independently driving cross-functional initiatives from problem definition to execution.
• Experience with Python, Git, or BI tools like Looker/Power BI/Tableau.
• Hands-on with data warehouses (BigQuery, Redshift, Snowflake).
• Familiarity with product analytics tools (Mixpanel, GA4, Amplitude).
• Exposure to forecasting, financial modeling, or experimentation (A/B testing).

Why Join Us?

• Work directly with top leadership in a high-impact role.
• Be part of an innovative and fast-growing AI startup.
• Opportunity to take ownership of key projects and drive efficiency.
• A collaborative, ambitious, and fast-paced work environment.
• Perks & Benefits: gym membership benefit, workation policy, and company-sponsored lunch.

If you’re looking for an exciting role that combines strategy, execution, and leadership exposure, we’d love to hear from you!

Apply now to join VerbaFlo.AI on this journey.