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About This Role
Greetings from Ashra Technologies
We are hiring....
Job Role: Senior AI Engineer/Data Scientist
Experience: 5\+years
Location: Nashville , TN, US
We are hiring a pioneering, fully autonomous Senior AI Engineer/Data Scientist to own the complete data science lifecycle for our flagship prediction model project. This role is mission\-critical — the candidate will be the single point of expertise responsible for sourcing, analyzing, and engineering all data that powers our predictive models. Operating independently with minimal supervision, this individual must combine deep AI/ML mastery, hands\-on engineering skills, and sharp business acumen to deliver measurable, production\-grade outcomes.
Key Responsibilities:
Data Analysis \& Pipeline Ownership
▸ Lead end\-to\-end analysis of large, complex, multi\-source datasets to surface patterns driving model inputs
▸ Identify, collect, clean, validate, and transform all data required for prediction model consumption
▸ Design and maintain scalable, production\-grade data pipelines (training, validation, inference)
▸ Perform deep EDA, data profiling, and quality audits to ensure model\-ready data standards
Predictive Modeling \& AI/ML
▸ Architect, train, evaluate, and iterate ML models — supervised, unsupervised, and reinforcement learning
▸ Own feature engineering: selection, extraction, transformation, and dimensionality reduction
▸ Apply advanced techniques: deep learning, NLP, time\-series forecasting, ensemble methods
▸ Benchmark, A/B test, and monitor models in production; drive continuous performance improvement
▸ Deploy models via REST APIs (FastAPI/Flask); ensure reproducibility and scalability
Independent Ownership \& Leadership
▸ Self\-direct from problem definition through solution delivery with zero hand\-holding
▸ Translate ambiguous business problems into precise, executable data science problem statements
▸ Communicate model results and data insights clearly to technical and non\-technical stakeholders
▸ Document all experiments, methodologies, and outcomes — audit\-ready and reproducible
▸ Champion best practices across the data science lifecycle; mentor junior team members
QUALIFICATIONS
▸ B.S./M.S./Ph.D. in Computer Science, Statistics, Mathematics, or equivalent quantitative field (Master's/Ph.D. strongly preferred)
▸ 5\+ years of hands\-on data science experience with at least 2 years delivering production\-grade ML models
▸ Proven ability to own and deliver end\-to\-end data science projects independently
▸ Portfolio demonstrating innovation in predictive modeling and measurable business impact
▸ Kaggle rankings, research publications, or open\-source ML contributions are a strong plus
▸ Experience in a fast\-paced, data\-driven, decision\-model environment
REQUIRED SKILLS \& QUALIFICATIONS
Core Data Science \& Mathematics
▸ Statistics (Bayesian inference, hypothesis testing, regression, distributions)
▸ Linear algebra, calculus, and probability applied to ML model design
▸ Supervised \& unsupervised learning, anomaly detection, clustering
▸ Time\-series analysis \& forecasting: ARIMA, Prophet, LSTM
Programming \& Development
▸ Python (Expert): NumPy, Pandas, Scikit\-learn, Statsmodels, Matplotlib, Plotly
▸ SQL (Advanced): window functions, CTEs, query optimization
▸ Git / GitHub; CI/CD for ML; MLOps with MLflow or Kubeflow
▸ Docker \& Kubernetes for model containerization and serving
AI / ML Frameworks (Must\-Have)
▸ TensorFlow and/or PyTorch — deep learning architectures
▸ XGBoost, LightGBM, CatBoost — gradient boosting \& ensemble methods
▸ Hugging Face Transformers — NLP, LLMs, and fine\-tuning
▸ SHAP, LIME — model explainability and interpretability
▸ LLMs / Generative AI / Prompt Engineering — strong advantage
Cloud \& Data Infrastructure
▸ AWS (SageMaker, S3, Glue), GCP (Vertex AI, BigQuery), or Azure ML
▸ Apache Spark / PySpark — distributed data processing
▸ Airflow / Prefect — pipeline orchestration
Snowflake (Good to Have)
▸ Snowflake Data Cloud: querying, Snowpark for Python ML pipelines
▸ Snowflake Cortex AI / ML Functions for in\-database ML
▸ dbt for data transformation; data governance within Snowflake
Interested can share cv at: [email protected]/8688322631
Pay: $133,192\.53 \- $160,403\.92 per year
Experience:
- Data science: 5 years (Preferred)
- AI: 5 years (Preferred)
Work Location: Remote
Salary Context
This $133K-$160K range is below the median for Data Scientist roles in our dataset (median: $160K across 258 roles with salary data).
View full Data Scientist salary data →Role Details
About This Role
Data Scientists extract insights and build predictive models from data. In the AI era, many roles now include LLM-powered analytics, automated reporting, and integration with generative AI tools. The role has evolved from 'the person who runs SQL queries' to 'the person who builds AI-powered data products.'
Modern data science roles fall into two camps: analytics-focused (insights, dashboards, experimentation) and ML-focused (building predictive models, recommendation systems, NLP features). The best data scientists can operate in both modes. The AI shift means that even analytics-focused roles now involve building automated insight pipelines using LLMs, going well beyond one-off reports.
Across the 4,317 AI roles we're tracking, Data Scientist positions make up 8% of the market. At Ashra Technologies Pvt Ltd, this role fits into their broader AI and engineering organization.
Data Scientist roles remain in high demand, though the definition keeps shifting. Companies increasingly want candidates who can bridge traditional statistics with modern ML and LLM capabilities. The 'pure insights' data scientist role is consolidating into analytics engineering, while the 'build models' data scientist role is merging with ML engineering.
What the Work Looks Like
A typical week includes: analyzing experiment results for a product feature launch, building a predictive model for customer churn, creating an automated reporting pipeline using LLM-powered summarization, presenting insights to stakeholders, and cleaning data (always cleaning data). The ratio of analysis to engineering varies by company, but expect both.
Data Scientist roles remain in high demand, though the definition keeps shifting. Companies increasingly want candidates who can bridge traditional statistics with modern ML and LLM capabilities. The 'pure insights' data scientist role is consolidating into analytics engineering, while the 'build models' data scientist role is merging with ML engineering.
Skills Required
Python, SQL, and statistical modeling are the foundation. Increasingly, roles want experience with LLMs for data analysis, automated insight generation, and building AI-powered data products. Familiarity with cloud data platforms (Snowflake, BigQuery, Databricks) and ML frameworks (scikit-learn, PyTorch) covers most job requirements.
Experimentation design and causal inference are underrated skills that separate strong candidates. Companies care about whether their product changes cause improvements, and can distinguish causation from correlation. A/B testing methodology, Bayesian statistics, and the ability to communicate uncertainty to non-technical stakeholders are high-value skills.
Good postings specify the data stack, the types of problems you'll work on, and the team structure. Look for companies that differentiate between analytics and ML data science. Vague 'data scientist' postings that list every skill under the sun usually mean the company doesn't know what they need.
Compensation Benchmarks
Data Scientist roles pay a median of $192,890 based on 789 positions with disclosed compensation. Senior-level AI roles across all categories have a median of $227,400. This role's midpoint ($146K) sits 24% below the category median. Disclosed range: $133K to $160K.
Across all AI roles, the market median is $215,000. Top-quartile compensation starts at $266,300. The 90th percentile reaches $320,790. For comparison, the highest-paying categories include AI Safety ($287,500) and Research Engineer ($272,100). By seniority level: Entry: $110,000; Mid: $194,400; Senior: $227,400; Director: $274,554; VP: $241,000.
Ashra Technologies Pvt Ltd AI Hiring
Ashra Technologies Pvt Ltd has 1 open AI role right now. They're hiring across Data Scientist. Based in Remote, US. Compensation range: $160K - $160K.
Remote Work Context
Remote AI roles pay a median of $180,000 across 1,196 positions. About 15% of all AI roles offer remote work.
Career Path
Common paths into Data Scientist roles include Data Analyst, Statistician, Quantitative Researcher.
From here, career progression typically leads toward Senior Data Scientist, ML Engineer, AI Product Manager.
Start with statistics and SQL. Build a real analysis project on public data that demonstrates insight generation alongside model building. The market values data scientists who can communicate findings clearly to business stakeholders. If you want to move toward ML engineering, invest in software engineering fundamentals and production deployment skills.
What to Expect in Interviews
Interviews combine statistics, coding, and business acumen. SQL is almost always tested, often with complex joins and window functions. Expect a case study round where you're given a business problem and asked to design an analysis plan. Coding rounds focus on pandas, statistical modeling, and visualization. The strongest differentiator is how well you communicate insights to non-technical stakeholders during presentation rounds.
When evaluating opportunities: Good postings specify the data stack, the types of problems you'll work on, and the team structure. Look for companies that differentiate between analytics and ML data science. Vague 'data scientist' postings that list every skill under the sun usually mean the company doesn't know what they need.
AI Hiring Overview
The AI job market has 4,317 open positions tracked in our dataset. By seniority: 138 entry-level, 2,071 mid-level, 1,655 senior, and 453 leadership roles (Director, VP, C-Level). Remote roles make up 15% of the market (635 positions). The remaining 3,657 roles require on-site or hybrid attendance.
The market median for AI roles is $215,000. Top-quartile compensation starts at $266,300. The 90th percentile reaches $320,790. Highest-paying categories: AI Safety ($287,500 median, 34 roles); Research Engineer ($272,100 median, 227 roles); AI Engineering Manager ($244,000 median, 23 roles).
Data Scientist roles remain in high demand, though the definition keeps shifting. Companies increasingly want candidates who can bridge traditional statistics with modern ML and LLM capabilities. The 'pure insights' data scientist role is consolidating into analytics engineering, while the 'build models' data scientist role is merging with ML engineering.
The AI Job Market Today
The AI job market spans 4,317 open positions across 15 role categories. The largest categories by volume: AI/ML Engineer (3,004), Data Scientist (345), AI Software Engineer (309). These three account for the majority of open positions, though smaller categories often have higher per-role compensation because of specialized skill requirements.
The seniority mix tells a story about where AI teams are in their maturity. Entry-level roles (138) are outnumbered by mid-level (2,071) and senior (1,655) positions, reflecting that most companies are past the 'build a team from scratch' phase and need experienced engineers who can ship production systems. Leadership roles (Director, VP, C-Level) total 453 positions, representing the bottleneck between technical execution and organizational strategy.
Remote work availability sits at 15% of all AI roles (635 positions), with 3,657 requiring on-site or hybrid attendance. The remote share has stabilized after the post-pandemic correction. Senior and specialized roles (Research Scientist, ML Architect) are more likely to be remote-eligible than entry-level positions, partly because experienced hires have more negotiating power and partly because these roles require less hands-on mentorship.
AI compensation is structured in clear tiers. The market median sits at $215,000. Top-quartile roles start at $266,300, and the 90th percentile reaches $320,790. These figures include base salary with disclosed compensation. Total compensation (including equity, bonuses, and sign-on) runs 20-40% higher at companies that offer those components.
Category matters for compensation. AI Safety roles lead at $287,500 median, while Prompt Engineer roles sit at $145,000. The spread between highest and lowest-paying categories reflects the premium on specialized technical skills versus broader analytical roles.
The most in-demand skills across all AI postings: Python (2,249 postings), Aws (1,224 postings), Azure (938 postings), Rag (915 postings), Gcp (660 postings), Pytorch (640 postings), Prompt Engineering (624 postings), Kubernetes (559 postings). Python dominates, appearing in the vast majority of role descriptions regardless of category. Cloud platform experience (AWS, GCP, Azure) is the second most common requirement. The newer entrants to the top skills list (RAG, vector databases, LLM APIs) reflect the shift from traditional ML toward generative AI applications.
Frequently Asked Questions
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