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About This Role
Introduction:
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We are seeking a Data Scientist to join our team. The Data Scientist will be responsible for designing, developing, evaluating, and optimizing machine learning models that solve complex business problems for our enterprise clients. This role collaborates closely with business partners, engineers, and client stakeholders to deliver scalable AI solutions while ensuring models continue to perform effectively in production environments.
What You'll Do:
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- Collaborate with business and technical teams to define machine learning objectives, success metrics, and measurable outcomes.
- Identify, ingest, transform, and enrich structured and unstructured data for model development.
- Engineer features from structured and text\-based data, including entity extraction, normalization, embeddings, and feature generation.
- Apply statistical and machine learning techniques including classification, regression, clustering, and deep learning models.
- Design and execute experiments including A/B testing, hypothesis testing, causal analysis, and model benchmarking.
- Build production\-ready machine learning models using Python and Spark.
- Design automated evaluation methodologies including benchmark datasets, acceptance tests, and promotion gates.
- Monitor production model performance, identify model drift and degradation, and continuously improve model accuracy through retraining and optimization.
- Develop scalable machine learning solutions that integrate with enterprise AI platforms.
- Present methodologies, findings, and recommendations to both technical teams and executive stakeholders.
Who You Are:
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- Passionate about solving business problems through data science and artificial intelligence.
- Experienced working with large\-scale enterprise data environments.
- Comfortable owning machine learning models throughout their lifecycle from development through production.
- Strong analytical thinker with excellent problem\-solving abilities.
- Fast learner with attention to detail.
- Outstanding verbal and written communication skills.
- Able to collaborate effectively with cross\-functional teams and client stakeholders.
Education: Bachelor's or Master's degree in Data Science, Computer Science, Statistics, Mathematics, Engineering, or another quantitative field.
Related Work Experience: 3\+ years of experience in Data Science, Machine Learning, or Advanced Analytics.
Technical Skills:
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- Advanced SQL
- Python (NumPy, Pandas, Scikit\-learn, TensorFlow, PyTorch)
- Apache Spark / PySpark
- AWS SageMaker (Databricks, Azure ML, or Vertex AI experience is a plus)
- Natural Language Processing (NLP), embeddings, entity extraction, and feature engineering
- Statistical modeling, regression, experimental design, hypothesis testing, and drift analysis
- Automated testing and data validation
- Experience deploying and supporting production machine learning models
- Git and modern software development practices
Our Purpose and Culture:
At BDIPlus, our mission is to help enterprises utilize their resources more efficiently, implement effective information management and empower them by enabling richer insights and intelligence.
We are driven by a single purpose: empower the technology transformation. We are passionate about creating foundational technology platforms for enterprise data and information management. Our employees are at the heart of the work we do at BDIPlus. We are committed to encouraging and celebrating innovation, creativity, and hard work among our team members.
Working at BDIPlus offers:
- A diverse, fun to work with, highly intelligent and innovative team.
- Full health and commuter benefits.
- A competitive salary and an annual bonus.
- Standard time off, sick leave, and time off on all national holidays.
- Visa sponsorship
- An environment where creative thinking is encouraged and innovation is a driving force of everything we do.
Salary Context
This $115K-$140K 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 BDIPlus, 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. Mid-level AI roles across all categories have a median of $194,400. This role's midpoint ($127K) sits 34% below the category median. Disclosed range: $115K to $140K.
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.
BDIPlus AI Hiring
BDIPlus has 2 open AI roles right now. They're hiring across AI/ML Engineer, Data Scientist. Based in New York, NY, US. Compensation range: $130K - $140K.
Location Context
AI roles in New York pay a median of $220,000 across 1,650 tracked positions.
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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