ML Engineer

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. 

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.

Data Scientist – Machine Learning & Applied Analytics. 

Location: Bengaluru 

Experience: 5–8 Years 

Role Type: Full-Time 

Business Unit: Products & Platforms (PMG) 

Role Overview 

We are looking for a Data Scientist – Product Analytics & Machine Learning to help build differentiated analytics and ML capabilities across our product and platform portfolio, including MOBIUS+ and Signate. 

This is a hands-on product-focused role responsible for translating real-world customer and domain problems into scalable analytics and machine learning solutions. The role will work across the complete lifecycle — from data exploration and experimentation through model development, validation, productization and production monitoring. 

The Data Scientist will also work closely with Tata Elxsi's internal analytics teams, specialist solution providers and academic/research institutions to evaluate, integrate and evolve analytical capabilities as part of our products. 

Key Responsibilities 

Product Analytics & ML 

• Translate product and customer problems into well-defined analytics and ML use cases. 
• Perform data exploration, feature engineering, statistical analysis and experimentation. 
• Develop, train, validate and optimize machine learning and predictive models. 
• Define appropriate model performance metrics, benchmarks and acceptance criteria. 
• Convert prototypes and experiments into scalable, production-ready capabilities. 
• Work with Product, Architecture and Engineering teams to integrate models and analytics into SaaS products and platforms. 

Model Evaluation & Productization 

• Evaluate models and analytical solutions developed by internal teams, technology partners and specialist solution providers. 
• Assess model assumptions, data requirements, accuracy, robustness, explainability and limitations. 
• Benchmark alternative models/approaches and provide evidence-based recommendations. 
• Establish practices for model versioning, validation, monitoring, drift detection and continuous improvement. 
• Ensure that externally sourced models can be effectively integrated, governed and evolved within the product. 

Collaboration & Research 

• Collaborate with Tata Elxsi's broader AI, analytics and domain teams and leverage specialist capabilities where required. 
• Work technically with external solution providers to understand and integrate their algorithms and models. 
• Engage with researchers and academic institutions such as IITs, NITs and IISc on advanced analytics and ML opportunities. 
• Evaluate research, algorithms and PoCs and help translate promising approaches into production-grade product capabilities. 
• Build reusable analytical assets, datasets, features, evaluation frameworks and know-how across the product portfolio. 

Technical Skills 

• Strong foundation in statistics, probability, machine learning and data analysis. 
• Strong hands-on programming skills in Python and common data science libraries. 
• Experience with Pandas, NumPy, scikit-learn and relevant ML frameworks. 
• Experience developing predictive models using structured and time-series data. 
• Understanding of supervised/unsupervised learning, anomaly detection, forecasting, classification and regression techniques. 
• Experience with feature engineering, model selection, hyperparameter optimization and model evaluation. 
• Good understanding of model explainability, bias, uncertainty and robustness. 
• Working knowledge of SQL and large-scale data processing. 
• Experience deploying or integrating ML models into production applications through APIs, services or ML pipelines. 
• Understanding of MLOps, including model versioning, experiment tracking, monitoring and model lifecycle management. 
• Experience with cloud environments, preferably AWS, is desirable. 
• Exposure to GenAI/LLMs and their application within product analytics is an advantage. 

Product & Engineering Mindset 

• Ability to move beyond experimentation and think about how analytics becomes a repeatable product capability. 
• Strong problem-solving ability and comfort working with incomplete or imperfect real-world datasets. 
• Ability to distinguish between an interesting analytical result and one that creates measurable customer/product value. 
• Comfortable working closely with Product Managers, Domain SMEs, Architects and Software Engineers. 
• Ability to critically evaluate third-party algorithms and models rather than treating external solutions as black boxes. 
• Strong technical communication skills and ability to explain analytical concepts and model outcomes to both technical and business stakeholders. 

Preferred Experience 

• 5–8 years of relevant experience in Data Science, Machine Learning, Applied Analytics or related roles. 
• Experience building ML/analytics capabilities that have been deployed into production products or platforms. 
• Experience working with automotive, mobility, connected vehicle, battery/EV, IoT or industrial datasets is desirable but not mandatory. 
• Experience collaborating with technology partners, research organizations or academic institutions is an advantage. 
• Master's degree in Computer Science, Data Science, Statistics, Mathematics, Engineering or a related quantitative discipline preferred. 

What Success Looks Like 

The successful candidate will help PMG establish a strong internal Data Science & ML capability that can build differentiated analytics, intelligently leverage Tata Elxsi's broader capabilities and external specialists, and retain the knowledge and ownership required to continuously evolve analytics as a core part of our products and platforms. 

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.