Interested in this Data Scientist role at Focus Camera?
Apply Now →Skills & Technologies
About This Role
Job description:
*This role is not eligible for employer sponsored visas. Applicants must be legally authorized to work in the United States without the need for company sponsorship now or in the future.*
Jr. Data Scientist
Location: Brooklyn, NY (4 days On\-site / 1 day WFH)
About Us:
At Focus Camera, we fuel creativity, support our partners, and deliver exceptional customer experience. Guided by our core values—teamwork, responsibility, excellence, adaptability, and results—we collaborate to build scalable solutions that drive growth.
Founded in 1966 as a family\-owned business, Focus Camera has grown to 150\+ employees across three locations: our Brooklyn headquarters and store, our 90,000 sq. ft. warehouse in North Brunswick, NJ, and our Lakewood, NJ retail store. We specialize in consumer electronics and pro audio, partnering with leading brands such as Sony, Nikon, Fujifilm, Yamaha, Fender, and Ninja selling thousands of products across several online platforms such as Amazon, Best Buy, and Walmart.
Role Overview:
The Data Science Team supports the company’s ecommerce platforms and adjacent teams by managing advertising campaigns and ensuring high quality reporting processes. This role will assist other departments with as\-needed KPI reporting\- sales, advertising expenditure, etc. The initial focus will be accurate reporting and understanding of internal data processes. Eventually, the role will help to maintain reporting pipelines and dashboards and expand on these further.
The role will need to communicate analyses results/reports to other departments and executives regularly.
Responsibilities:
- Extract, transform, and analyze data from advertising platforms (e.g., Google Ads, Amazon Ads) and internal databases using SQL and Python to identify trends, anomalies, and optimization opportunities.
- Design, build, and maintain production\-grade ETL pipelines using SQL and Python; improve reliability, scalability, and data quality.
- Own the end\-to\-end development of machine learning models, including problem formulation, feature engineering, training, evaluation, deployment, and iteration.
- Develop and experiment with advanced modeling techniques, including reinforcement learning, regression, and other statistical/ML approaches to improve marketing and sales outcomes.
- Maintain and enhance internal dashboards and reporting tools used to track advertising and business performance (e.g., Power BI).
- Perform ad\-hoc analysis to support strategic decisions across marketing, growth, and operations.
Requirements:
- 2\+ years of experience using Python for data ingestion, analysis, and machine learning (R acceptable background, but Python is required for this role)
- 2\+ years of experience using SQL for data analysis and data pipeline development
- MySQL and/or Microsoft SQL Server preferred
- 1\+ years of hands\-on data engineering experience, including building and maintaining ETL pipelines
- Proven experience owning and building machine learning models from scratch (not just tuning or consuming existing models)
- 1\+ years of experience with cloud platforms such as AWS and/or Azure
- Experience working with version control systems (Git/GitHub) in a collaborative environment
- Strong understanding of data modeling, validation, and analytics best practices
Nice\-to\-have:
- Experience with reinforcement learning or experimentation\-driven ML systems
- Hands\-on experience with Google Ads, Amazon Ads, or other eCommerce advertising platforms
- Familiarity with SEO analytics and organic search performance measurement
- Experience with Power BI or similar dashboarding/BI tools
- Experience working with large\-scale marketing or eCommerce datasets
- Background in Bayesian statistics, regression analysis, or causal inference
Exposure to GCP in addition to AWS/Azure
- Collaborate closely with cross\-functional partners (marketing, product, engineering) to understand requirements and deliver actionable insights.
- Contribute to process improvements, data architecture decisions, and best practices for analytics, ML, and data engineering workflows.
- Support and help implement new data\-driven strategies to achieve sales and marketing goals.
Benefits:
- 401(k)
- Health insurance
- Paid time off
Salary Context
This $70K-$85K range is in the lower quartile 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 Focus Camera, 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. Entry-level AI roles across all categories have a median of $110,000. This role's midpoint ($77K) sits 60% below the category median. Disclosed range: $70K to $85K.
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.
Focus Camera AI Hiring
Focus Camera has 1 open AI role right now. They're hiring across Data Scientist. Based in Brooklyn, NY, US. Compensation range: $85K - $85K.
Location Context
Across all AI roles, 15% (635 positions) offer remote work, while 3,657 require on-site attendance. Top AI hiring metros: New York (1,650 roles, $220,000 median); San Francisco (1,335 roles, $265,000 median); Los Angeles (708 roles, $214,112 median).
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
Get Weekly AI Career Intelligence
Salary data, skills demand, and market signals from 16,000+ AI job postings. Every Monday.